no code implementations • CCL 2021 • Mingyue Zhou, Chen Gong, Zhenghua Li, Min Zhang
“数据标注最重要的考虑因素是数据的质量和标注代价。我们调研发现自然语言处理领域的数据标注工作通常采用机标人校的标注方法以降低代价;同时, 很少有工作严格对比不同标注方法, 以探讨标注方法对标注质量和代价的影响。该文借助一个成熟的标注团队, 以依存句法数据标注为案例, 实验对比了机标人校、双人独立标注、及本文通过融合前两种方法所新提出的人机独立标注方法, 得到了一些初步的结论。”
no code implementations • IWSLT (ACL) 2022 • Minghan Wang, Jiaxin Guo, Yinglu Li, Xiaosong Qiao, Yuxia Wang, Zongyao Li, Chang Su, Yimeng Chen, Min Zhang, Shimin Tao, Hao Yang, Ying Qin
The cascade system is composed of a chunking-based streaming ASR model and the SimulMT model used in the T2T track.
no code implementations • ACL 2022 • Ying Li, Shuaike Li, Min Zhang
To address this issue, we for the first time apply a dynamic matching network on the shared-private model for semi-supervised cross-domain dependency parsing.
1 code implementation • ACL 2022 • Nan Yu, Meishan Zhang, Guohong Fu, Min Zhang
Pre-trained language models (PLMs) have shown great potentials in natural language processing (NLP) including rhetorical structure theory (RST) discourse parsing. Current PLMs are obtained by sentence-level pre-training, which is different from the basic processing unit, i. e. element discourse unit (EDU). To this end, we propose a second-stage EDU-level pre-training approach in this work, which presents two novel tasks to learn effective EDU representations continually based on well pre-trained language models. Concretely, the two tasks are (1) next EDU prediction (NEP) and (2) discourse marker prediction (DMP). We take a state-of-the-art transition-based neural parser as baseline, and adopt it with a light bi-gram EDU modification to effectively explore the EDU-level pre-trained EDU representation. Experimental results on a benckmark dataset show that our method is highly effective, leading a 2. 1-point improvement in F1-score. All codes and pre-trained models will be released publicly to facilitate future studies.
no code implementations • ACL 2022 • Dengji Guo, Zhengrui Ma, Min Zhang, Yang Feng
Regularization methods applying input perturbation have drawn considerable attention and have been frequently explored for NMT tasks in recent years.
no code implementations • SemEval (NAACL) 2022 • Yinglu Li, Min Zhang, Xiaosong Qiao, Minghan Wang
In order to verify whether our model could also perform better in subtask 2 (the regression subtask), the ranking score is transformed into classification labels by an up-sampling strategy.
1 code implementation • Findings (EMNLP) 2021 • Qingrong Xia, Zhenghua Li, Rui Wang, Min Zhang
In particular, one recent seq-to-seq work directly fine-tunes AMR graph sequences on the encoder-decoder pre-trained language model and achieves new state-of-the-art results, outperforming previous works by a large margin.
no code implementations • Findings (EMNLP) 2021 • Ying Li, Meishan Zhang, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan
Thanks to the strong representation learning capability of deep learning, especially pre-training techniques with language model loss, dependency parsing has achieved great performance boost in the in-domain scenario with abundant labeled training data for target domains.
no code implementations • INLG (ACL) 2021 • Minghan Wang, Guo Jiaxin, Yuxia Wang, Yimeng Chen, Su Chang, Daimeng Wei, Min Zhang, Shimin Tao, Hao Yang
Mask-predict CMLM (Ghazvininejad et al., 2019) has achieved stunning performance among non-autoregressive NMT models, but we find that the mechanism of predicting all of the target words only depending on the hidden state of [MASK] is not effective and efficient in initial iterations of refinement, resulting in ungrammatical repetitions and slow convergence.
no code implementations • EMNLP 2020 • Lijie Wang, Ao Zhang, Kun Wu, Ke Sun, Zhenghua Li, Hua Wu, Min Zhang, Haifeng Wang
This paper describes in detail the construction process and data statistics of DuSQL.
no code implementations • IWSLT (ACL) 2022 • Minghan Wang, Jiaxin Guo, Xiaosong Qiao, Yuxia Wang, Daimeng Wei, Chang Su, Yimeng Chen, Min Zhang, Shimin Tao, Hao Yang, Ying Qin
For machine translation part, we pretrained three translation models on WMT21 dataset and fine-tuned them on in-domain corpora.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • IWSLT (ACL) 2022 • Jiaxin Guo, Yinglu Li, Minghan Wang, Xiaosong Qiao, Yuxia Wang, Hengchao Shang, Chang Su, Yimeng Chen, Min Zhang, Shimin Tao, Hao Yang, Ying Qin
The paper presents the HW-TSC’s pipeline and results of Offline Speech to Speech Translation for IWSLT 2022.
no code implementations • SemEval (NAACL) 2022 • Xiaosong Qiao, Yinglu Li, Min Zhang, Minghan Wang, Hao Yang, Shimin Tao, Qin Ying
This paper describes the system for the identifying Plausible Clarifications of Implicit and Underspecified Phrases.
1 code implementation • Findings (NAACL) 2022 • Huan Lin, Baosong Yang, Liang Yao, Dayiheng Liu, Haibo Zhang, Jun Xie, Min Zhang, Jinsong Su
Diverse NMT aims at generating multiple diverse yet faithful translations given a source sentence.
no code implementations • COLING 2022 • Zijie Lin, Bin Liang, Yunfei Long, Yixue Dang, Min Yang, Min Zhang, Ruifeng Xu
This essentially allows the framework to understand the appropriate graph structures for learning intricate relations among different modalities.
no code implementations • EMNLP (BlackboxNLP) 2021 • Minghan Wang, Guo Jiaxin, Yuxia Wang, Yimeng Chen, Su Chang, Hengchao Shang, Min Zhang, Shimin Tao, Hao Yang
Length prediction is a special task in a series of NAT models where target length has to be determined before generation.
1 code implementation • EMNLP 2021 • Xincheng Ju, Dong Zhang, Rong Xiao, Junhui Li, Shoushan Li, Min Zhang, Guodong Zhou
Therefore, in this paper, we are the first to jointly perform multi-modal ATE (MATE) and multi-modal ASC (MASC), and we propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-modal aspect-level sentiment analysis (MALSA).
no code implementations • EMNLP 2021 • Xinglin Lyu, Junhui Li, ZhengXian Gong, Min Zhang
In this paper we apply “one translation per discourse” in NMT, and aim to encourage lexical translation consistency for document-level NMT.
no code implementations • MTSummit 2021 • Minghan Wang, Jiaxin Guo, Yimeng Chen, Chang Su, Min Zhang, Shimin Tao, Hao Yang
Based on large-scale pretrained networks and the liability to be easily overfitting with limited labelled training data of multimodal translation (MMT) is a critical issue in MMT.
no code implementations • Findings (ACL) 2022 • Yuxia Wang, Minghan Wang, Yimeng Chen, Shimin Tao, Jiaxin Guo, Chang Su, Min Zhang, Hao Yang
Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels due to its subjectivity.
1 code implementation • CoNLL (EMNLP) 2021 • Yang Hou, Houquan Zhou, Zhenghua Li, Yu Zhang, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan
In the coarse labeling stage, the joint model outputs a bracketed tree, in which each node corresponds to one of four labels (i. e., phrase, subphrase, word, subword).
no code implementations • Findings (ACL) 2022 • Kehai Chen, Masao Utiyama, Eiichiro Sumita, Rui Wang, Min Zhang
Machine translation typically adopts an encoder-to-decoder framework, in which the decoder generates the target sentence word-by-word in an auto-regressive manner.
no code implementations • WMT (EMNLP) 2021 • Daimeng Wei, Zongyao Li, Zhanglin Wu, Zhengzhe Yu, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Minghan Wang, Lizhi Lei, Min Zhang, Hao Yang, Ying Qin
This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT 2021 News Translation Shared Task.
no code implementations • WMT (EMNLP) 2021 • Zongyao Li, Daimeng Wei, Hengchao Shang, Xiaoyu Chen, Zhanglin Wu, Zhengzhe Yu, Jiaxin Guo, Minghan Wang, Lizhi Lei, Min Zhang, Hao Yang, Ying Qin
This paper presents the submission of Huawei Translation Service Center (HW-TSC) to WMT 2021 Triangular MT Shared Task.
no code implementations • WMT (EMNLP) 2021 • Zhengzhe Yu, Daimeng Wei, Zongyao Li, Hengchao Shang, Xiaoyu Chen, Zhanglin Wu, Jiaxin Guo, Minghan Wang, Lizhi Lei, Min Zhang, Hao Yang, Ying Qin
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to the WMT 2021 Large-Scale Multilingual Translation Task.
no code implementations • WMT (EMNLP) 2021 • Hao Yang, Zhanglin Wu, Zhengzhe Yu, Xiaoyu Chen, Daimeng Wei, Zongyao Li, Hengchao Shang, Minghan Wang, Jiaxin Guo, Lizhi Lei, Chuanfei Xu, Min Zhang, Ying Qin
This paper describes the submission of Huawei Translation Service Center (HW-TSC) to WMT21 biomedical translation task in two language pairs: Chinese↔English and German↔English (Our registered team name is HuaweiTSC).
no code implementations • WMT (EMNLP) 2021 • Yimeng Chen, Chang Su, Yingtao Zhang, Yuxia Wang, Xiang Geng, Hao Yang, Shimin Tao, Guo Jiaxin, Wang Minghan, Min Zhang, Yujia Liu, ShuJian Huang
This paper presents our work in WMT 2021 Quality Estimation (QE) Shared Task.
1 code implementation • COLING 2022 • Nan Yu, Guohong Fu, Min Zhang
It is believed that speaker interactions are helpful for this task.
Ranked #2 on Discourse Parsing on STAC
1 code implementation • COLING 2022 • Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, Jinsong Su
Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples.
no code implementations • 4 Jun 2024 • Junlin Lee, Yequan Wang, Jing Li, Min Zhang
Multimodal reasoning with large language models (LLMs) often suffers from hallucinations and the presence of deficient or outdated knowledge within LLMs.
no code implementations • 3 Jun 2024 • Yi Su, Yunpeng Tai, Yixin Ji, Juntao Li, Bowen Yan, Min Zhang
Large Language Models (LLMs) have demonstrated an impressive capability known as In-context Learning (ICL), which enables them to acquire knowledge from textual demonstrations without the need for parameter updates.
1 code implementation • 31 May 2024 • Miaomiao Cai, Lei Chen, Yifan Wang, Haoyue Bai, Peijie Sun, Le Wu, Min Zhang, Meng Wang
To alleviate popularity bias, existing efforts focus on emphasizing unpopular items or separating the correlation between item representations and their popularity.
1 code implementation • 28 May 2024 • Jiayu Li, Hanyu Li, Zhiyu He, Weizhi Ma, Peijie Sun, Min Zhang, Shaoping Ma
However, these libraries often impose certain restrictions on data and seldom support the same model to perform different tasks and input formats, limiting users from customized explorations.
no code implementations • 23 May 2024 • Weiqi Wu, Hongqiu Wu, Lai Jiang, XingYuan Liu, Jiale Hong, Hai Zhao, Min Zhang
Drama is a form of storytelling inspired by human creativity, proceeding with a predefined storyline, carrying emotions and thoughts.
1 code implementation • 22 May 2024 • Xiang Geng, Ming Zhu, Jiahuan Li, Zhejian Lai, Wei Zou, Shuaijie She, Jiaxin Guo, Xiaofeng Zhao, Yinglu Li, Yuang Li, Chang Su, Yanqing Zhao, Xinglin Lyu, Min Zhang, Jiajun Chen, Hao Yang, ShuJian Huang
For the second issue, we propose a method comprising two synergistic components: low-rank adaptation for training to maintain the original LLM parameters, and recovery KD, which utilizes data generated by the chat LLM itself to recover the original knowledge from the frozen parameters.
1 code implementation • 20 May 2024 • Xiaobo Liang, Haoke Zhang, Helan Hu, Juntao Li, Jun Xu, Min Zhang
The rapid advancement of large language models has given rise to a plethora of applications across a myriad of real-world tasks, mainly centered on aligning with human intent.
1 code implementation • 18 May 2024 • Yunxin Li, Shenyuan Jiang, Baotian Hu, Longyue Wang, Wanqi Zhong, Wenhan Luo, Lin Ma, Min Zhang
Although the Mixture of Experts (MoE) architecture has been employed to efficiently scale large language and image-text models, these efforts typically involve fewer experts and limited modalities.
1 code implementation • 18 May 2024 • Zhuangzhuang He, Yifan Wang, Yonghui Yang, Peijie Sun, Le Wu, Haoyue Bai, Jinqi Gong, Richang Hong, Min Zhang
To tackle the above limitations, we propose a Double Correction Framework for Denoising Recommendation (DCF), which contains two correction components from views of more precise sample dropping and avoiding more sparse data.
1 code implementation • 17 May 2024 • Yixin Ji, Yang Xiang, Juntao Li, Wei Chen, Zhongyi Liu, Kehai Chen, Min Zhang
To address the challenges of low-rank compression in LLMs, we conduct empirical research on the low-rank characteristics of large models.
no code implementations • 14 May 2024 • Danshi Wang, Yidi Wang, Xiaotian Jiang, Yao Zhang, Yue Pang, Min Zhang
To improve LLM's capability in professional fields and stimulate its potential on complex tasks, the details of performing prompt engineering, establishing domain knowledge library, and implementing complex tasks are illustrated in this study.
1 code implementation • 9 May 2024 • Dan Qiao, Yi Su, Pinzheng Wang, Jing Ye, Wenjing Xie, Yuechi Zhou, Yuyang Ding, Zecheng Tang, Jikai Wang, Yixin Ji, Yue Wang, Pei Guo, Zechen Sun, Zikang Zhang, Juntao Li, Pingfu Chao, Wenliang Chen, Guohong Fu, Guodong Zhou, Qiaoming Zhu, Min Zhang
Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities. However, their massive number of parameters leads to high deployment requirements and incurs significant inference costs, which impedes their practical applications.
1 code implementation • 8 May 2024 • Yunxin Li, Baotian Hu, Haoyuan Shi, Wei Wang, Longyue Wang, Min Zhang
Large Multimodal Models (LMMs) have achieved impressive success in visual understanding and reasoning, remarkably improving the performance of mathematical reasoning in a visual context.
no code implementations • 7 May 2024 • Junchao Wu, Runzhe Zhan, Derek F. Wong, Shu Yang, Xuebo Liu, Lidia S. Chao, Min Zhang
The efficacy of an large language model (LLM) generated text detector depends substantially on the availability of sizable training data.
no code implementations • 30 Apr 2024 • Min Zhang, Haoxuan Li, Fei Wu, Kun Kuang
Out-of-distribution (OOD) problems in few-shot classification (FSC) occur when novel classes sampled from testing distributions differ from base classes drawn from training distributions, which considerably degrades the performance of deep learning models deployed in real-world applications.
no code implementations • 29 Apr 2024 • Zhenxi Song, Ruihan Qin, Huixia Ren, Zhen Liang, Yi Guo, Min Zhang, Zhiguo Zhang
Cross-center data heterogeneity and annotation unreliability significantly challenge the intelligent diagnosis of diseases using brain signals.
1 code implementation • 29 Apr 2024 • Xinyu Ma, Xuebo Liu, Derek F. Wong, Jun Rao, Bei Li, Liang Ding, Lidia S. Chao, DaCheng Tao, Min Zhang
Experimental results show that MMT models trained on our dataset exhibit a greater ability to exploit visual information than those trained on other MMT datasets.
no code implementations • 28 Apr 2024 • Daming Gao, Yang Bai, Min Cao, Hao Dou, Mang Ye, Min Zhang
Text-based person search (TBPS) aims to retrieve images of a specific person from a large image gallery based on a natural language description.
1 code implementation • 24 Apr 2024 • Zhiyu He, Jiayu Li, Weizhi Ma, Min Zhang, Yiqun Liu, Shaoping Ma
Meanwhile, EEG signals are collected with a portable device.
1 code implementation • 22 Apr 2024 • Jiayin Wang, Fengran Mo, Weizhi Ma, Peijie Sun, Min Zhang, Jian-Yun Nie
To address this oversight, we propose benchmarking LLMs from a user perspective in both dataset construction and evaluation designs.
1 code implementation • 17 Apr 2024 • Dongfang Li, Zhenyu Liu, Xinshuo Hu, Zetian Sun, Baotian Hu, Min Zhang
In this paper, we address this gap by presenting a comprehensive analysis of these compressed vectors, drawing parallels to the parameters trained with gradient descent, and introduce the concept of state vector.
no code implementations • 12 Apr 2024 • Peijie Sun, Yifan Wang, Min Zhang, Chuhan Wu, Yan Fang, Hong Zhu, Yuan Fang, Meng Wang
In summary, our contributions underscore the importance of stable model training frameworks and the efficacy of collaborative-enhanced models in predicting user spending behavior in mobile gaming.
2 code implementations • 8 Apr 2024 • Longhui Zhang, Dingkun Long, Meishan Zhang, Yanzhao Zhang, Pengjun Xie, Min Zhang
Experimental results on Chinese sequence labeling datasets demonstrate that the improved BABERT variant outperforms the vanilla version, not only on these tasks but also more broadly across a range of Chinese natural language understanding tasks.
no code implementations • 7 Apr 2024 • Yuang Li, Min Zhang, Mengxin Ren, Miaomiao Ma, Daimeng Wei, Hao Yang
Audio deepfake detection (ADD) is essential for preventing the misuse of synthetic voices that may infringe on personal rights and privacy.
no code implementations • 2 Apr 2024 • Dapeng Zhi, Peixin Wang, Si Liu, Luke Ong, Min Zhang
We also devise a simulation-guided approach for training NBCs, aiming to achieve tightness in computing precise certified lower and upper bounds.
no code implementations • 1 Apr 2024 • Shaorun Zhang, Zhiyu He, Ziyi Ye, Peijie Sun, Qingyao Ai, Min Zhang, Yiqun Liu
To address these challenges and provide a more comprehensive understanding of user affective experience and cognitive activity, we propose EEG-SVRec, the first EEG dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation.
1 code implementation • 30 Mar 2024 • Hongqiu Wu, Y. Wang, XingYuan Liu, Hai Zhao, Min Zhang
The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game rules and autonomously generate game-play processes.
no code implementations • 29 Mar 2024 • Hanyu Li, Weizhi Ma, Peijie Sun, Jiayu Li, Cunxiang Yin, Yancheng He, Guoqiang Xu, Min Zhang, Shaoping Ma
In CUT, user similarity in the target domain is adopted as a constraint for user transformation learning to filter the user collaborative information from the source domain.
1 code implementation • 27 Mar 2024 • Zhefan Wang, Weizhi Ma, Min Zhang
First, we propose and define the recommendability identification task, which investigates the need for recommendations in the current conversational context.
1 code implementation • 27 Mar 2024 • Jiayu Li, Peijie Sun, Chumeng Jiang, Weizhi Ma, Qingyao Ai, Min Zhang
In this paper, we provide a new perspective that takes situations as the preconditions for users' interactions.
1 code implementation • 27 Mar 2024 • Shenghao Yang, Weizhi Ma, Peijie Sun, Min Zhang, Qingyao Ai, Yiqun Liu, Mingchen Cai
Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance.
1 code implementation • 27 Mar 2024 • Shenghao Yang, Weizhi Ma, Peijie Sun, Qingyao Ai, Yiqun Liu, Mingchen Cai, Min Zhang
Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items.
no code implementations • 27 Mar 2024 • Dongfang Li, Zetian Sun, Baotian Hu, Zhenyu Liu, Xinshuo Hu, Xuebo Liu, Min Zhang
Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content.
1 code implementation • 21 Mar 2024 • Han Zhao, Min Zhang, Wei Zhao, Pengxiang Ding, Siteng Huang, Donglin Wang
In recent years, the application of multimodal large language models (MLLM) in various fields has achieved remarkable success.
no code implementations • 21 Mar 2024 • Haofei Zhao, Yilun Liu, Shimin Tao, Weibin Meng, Yimeng Chen, Xiang Geng, Chang Su, Min Zhang, Hao Yang
Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations, which is of great importance for the development of MT.
1 code implementation • 18 Mar 2024 • Xiaojie Li, Yibo Yang, Xiangtai Li, Jianlong Wu, Yue Yu, Bernard Ghanem, Min Zhang
To tackle these challenges, we present GenView, a controllable framework that augments the diversity of positive views leveraging the power of pretrained generative models while preserving semantics.
no code implementations • 17 Mar 2024 • Ruizhe Zhang, Haitao Li, Yueyue Wu, Qingyao Ai, Yiqun Liu, Min Zhang, Shaoping Ma
In recent years, the utilization of large language models for natural language dialogue has gained momentum, leading to their widespread adoption across various domains.
no code implementations • 12 Mar 2024 • Hongwei Zhang, Xiaoyin Xu, Dongsheng An, Xianfeng GU, Min Zhang
Backdoor attacks become a significant security concern for deep neural networks in recent years.
no code implementations • 27 Feb 2024 • Jiaqi Wang, Zhenxi Song, Zhengyu Ma, Xipeng Qiu, Min Zhang, Zhiguo Zhang
Reconstructing natural language from non-invasive electroencephalography (EEG) holds great promise as a language decoding technology for brain-computer interfaces (BCIs).
2 code implementations • 26 Feb 2024 • Yuyang Ding, Juntao Li, Pinzheng Wang, Zecheng Tang, Bowen Yan, Min Zhang
In the Named Entity Recognition (NER) task, recent advancements have seen the remarkable improvement of LLMs in a broad range of entity domains via instruction tuning, by adopting entity-centric schema.
1 code implementation • 26 Feb 2024 • Liangxin Liu, Xuebo Liu, Derek F. Wong, Dongfang Li, Ziyi Wang, Baotian Hu, Min Zhang
In this work, we propose a novel approach, termed SelectIT, that capitalizes on the foundational capabilities of the LLM itself.
1 code implementation • 23 Feb 2024 • Yuanqing Yu, Chongming Gao, Jiawei Chen, Heng Tang, Yuefeng Sun, Qian Chen, Weizhi Ma, Min Zhang
EasyRL4Rec seeks to facilitate the model development and experimental process in the domain of RL-based RSs.
1 code implementation • 23 Feb 2024 • Zhefan Wang, Yuanqing Yu, Wendi Zheng, Weizhi Ma, Min Zhang
LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks.
no code implementations • 23 Feb 2024 • Xinglin Lyu, Junhui Li, Yanqing Zhao, Daimeng Wei, Shimin Tao, Hao Yang, Min Zhang
In this paper, we propose an alternative adaptation approach, named Decoding-enhanced Multi-phase Prompt Tuning (DeMPT), to make LLMs discriminately model and utilize the inter- and intra-sentence context and more effectively adapt LLMs to context-aware NMT.
no code implementations • 22 Feb 2024 • Jiayu Li, Aixin Sun, Weizhi Ma, Peijie Sun, Min Zhang
This paper emphasizes the importance of dedicated analyses and methods for domain-specific characteristics for the recommender system studies.
no code implementations • 21 Feb 2024 • Yunxin Li, Xinyu Chen, Baotian Hu, Haoyuan Shi, Min Zhang
Evaluating and Rethinking the current landscape of Large Multimodal Models (LMMs), we observe that widely-used visual-language projection approaches (e. g., Q-former or MLP) focus on the alignment of image-text descriptions yet ignore the visual knowledge-dimension alignment, i. e., connecting visuals to their relevant knowledge.
1 code implementation • 21 Feb 2024 • Yunxin Li, Baotian Hu, Wenhan Luo, Lin Ma, Yuxin Ding, Min Zhang
For this setting, previous methods utilize visual and textual encoders to encode the image and keywords and employ a language model-based decoder to generate the product description.
no code implementations • 21 Feb 2024 • Yunxin Li, Xinyu Chen, Baotain Hu, Min Zhang
Long video understanding is a significant and ongoing challenge in the intersection of multimedia and artificial intelligence.
1 code implementation • 20 Feb 2024 • Shoutao Guo, Shaolei Zhang, Zhengrui Ma, Min Zhang, Yang Feng
We propose SiLLM, which delegates the two sub-tasks to separate agents, thereby incorporating LLM into SiMT.
1 code implementation • 19 Feb 2024 • Junru Lu, Siyu An, Min Zhang, Yulan He, Di Yin, Xing Sun
In the quest to facilitate the deep intelligence of Large Language Models (LLMs) accessible in final-end user-bot interactions, the art of prompt crafting emerges as a critical yet complex task for the average user.
no code implementations • 19 Feb 2024 • Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, DaCheng Tao
Large language models (LLMs) have significantly advanced the field of natural language processing, while the expensive memory and computation consumption impede their practical deployment.
no code implementations • 18 Feb 2024 • Min Zhang, Jianfeng He, Taoran Ji, Chang-Tien Lu
This serves as a reminder to carefully consider sensitivity and confidence in the pursuit of model fairness.
no code implementations • 15 Feb 2024 • Min Zhang, Sato Takumi, Jack Zhang, Jun Wang
Large Language Models (LLMs) excel in generating personalized content and facilitating interactive dialogues, showcasing their remarkable aptitude for a myriad of applications.
no code implementations • 12 Feb 2024 • Zhengsheng Guo, Zhiwei He, Wenxiang Jiao, Xing Wang, Rui Wang, Kehai Chen, Zhaopeng Tu, Yong Xu, Min Zhang
Motivated by the success of unsupervised neural machine translation (UNMT), we introduce an unsupervised sign language translation and generation network (USLNet), which learns from abundant single-modality (text and video) data without parallel sign language data.
no code implementations • 8 Feb 2024 • Ying Zang, Chenglong Fu, Runlong Cao, Didi Zhu, Min Zhang, WenJun Hu, Lanyun Zhu, Tianrun Chen
This pioneering work lays the groundwork for future research in semi-supervised learning for referring expression segmentation.
1 code implementation • 5 Feb 2024 • Yifan Wang, Peijie Sun, Weizhi Ma, Min Zhang, Yuan Zhang, Peng Jiang, Shaoping Ma
Fairness of recommender systems (RS) has attracted increasing attention recently.
no code implementations • 2 Feb 2024 • Meishan Zhang, Bin Wang, Hao Fei, Min Zhang
In nested Named entity recognition (NER), entities are nested with each other, and thus requiring more data annotations to address.
no code implementations • 30 Jan 2024 • Zhi Jing, Yongye Su, Yikun Han, Bo Yuan, Haiyun Xu, Chunjiang Liu, Kehai Chen, Min Zhang
This survey explores the synergistic potential of Large Language Models (LLMs) and Vector Databases (VecDBs), a burgeoning but rapidly evolving research area.
1 code implementation • 25 Jan 2024 • Haoze Wu, Omri Isac, Aleksandar Zeljić, Teruhiro Tagomori, Matthew Daggitt, Wen Kokke, Idan Refaeli, Guy Amir, Kyle Julian, Shahaf Bassan, Pei Huang, Ori Lahav, Min Wu, Min Zhang, Ekaterina Komendantskaya, Guy Katz, Clark Barrett
This paper serves as a comprehensive system description of version 2. 0 of the Marabou framework for formal analysis of neural networks.
no code implementations • 22 Jan 2024 • Keqin Peng, Liang Ding, Yancheng Yuan, Xuebo Liu, Min Zhang, Yuanxin Ouyang, DaCheng Tao
In this work, we first revisit the factors contributing to this variance from both data and model aspects, and find that the choice of demonstration is both data- and model-dependent.
no code implementations • 21 Jan 2024 • Yuang Li, Jiawei Yu, Yanqing Zhao, Min Zhang, Mengxin Ren, Xiaofeng Zhao, Xiaosong Qiao, Chang Su, Miaomiao Ma, Hao Yang
In this work, we connect the Whisper encoder with ChatGLM3 and provide in-depth comparisons of these two approaches using Chinese automatic speech recognition (ASR) and name entity recognition (NER) tasks.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • 16 Jan 2024 • Yachao Li, Junhui Li, Jing Jiang, Min Zhang
Our proposed translation mixed-instructions enable LLMs (Llama-2~7B and 13B) to maintain consistent translation performance from the sentence level to documents containing as many as 2048 tokens.
no code implementations • 11 Jan 2024 • Jiaxin Guo, Minghan Wang, Xiaosong Qiao, Daimeng Wei, Hengchao Shang, Zongyao Li, Zhengzhe Yu, Yinglu Li, Chang Su, Min Zhang, Shimin Tao, Hao Yang
Previous works usually adopt end-to-end models and has strong dependency on Pseudo Paired Data and Original Paired Data.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 2 Jan 2024 • Xiongri Shen, Zhenxi Song, Linling Li, Min Zhang, Lingyan Liang Honghai Liu, Demao Deng, Zhiguo Zhang
Early diagnosis of mild cognitive impairment (MCI) and subjective cognitive decline (SCD) utilizing multi-modal magnetic resonance imaging (MRI) is a pivotal area of research.
no code implementations • 19 Dec 2023 • Zhengyu Chen, Teng Xiao, Kun Kuang, Zheqi Lv, Min Zhang, Jinluan Yang, Chengqiang Lu, Hongxia Yang, Fei Wu
In this paper, we study the problem of the generalization ability of GNNs in Out-Of-Distribution (OOD) settings.
1 code implementation • 19 Dec 2023 • Pengxiang Ding, Qiongjie Cui, Min Zhang, Mengyuan Liu, Haofan Wang, Donglin Wang
Human motion forecasting, with the goal of estimating future human behavior over a period of time, is a fundamental task in many real-world applications.
1 code implementation • 15 Dec 2023 • Shuanghao Bai, Min Zhang, Wanqi Zhou, Siteng Huang, Zhirong Luan, Donglin Wang, Badong Chen
Therefore, in this paper, we first experimentally demonstrate that the unsupervised-trained VLMs can significantly reduce the distribution discrepancy between source and target domains, thereby improving the performance of UDA.
no code implementations • 15 Dec 2023 • Dapeng Zhi, Peixin Wang, Cheng Chen, Min Zhang
In this work, we present the first approach for robustness verification of DRL-based control systems by introducing reward martingales, which offer a rigorous mathematical foundation to characterize the impact of state perturbations on system performance in terms of cumulative rewards.
no code implementations • 12 Dec 2023 • Min Zhang, Jianfeng He, Shuo Lei, Murong Yue, Linhang Wang, Chang-Tien Lu
The meaning of complex phrases in natural language is composed of their individual components.
1 code implementation • 9 Dec 2023 • Ziyi Ye, Xiaohui Xie, Qingyao Ai, Yiqun Liu, Zhihong Wang, Weihang Su, Min Zhang
To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based relevance feedback with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking.
1 code implementation • 5 Dec 2023 • Xinyu Ma, Xuebo Liu, Min Zhang
In multilingual translation research, the comprehension and utilization of language families are of paramount importance.
1 code implementation • 28 Nov 2023 • Longhui Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang, Min Zhang
In this work, we propose a two-stage progressive paradigm to better adapt LLMs to text ranking.
no code implementations • 27 Nov 2023 • Yunxin Li, Baotian Hu, Wei Wang, Xiaochun Cao, Min Zhang
These models predominantly map visual information into language representation space, leveraging the vast knowledge and powerful text generation abilities of LLMs to produce multimodal instruction-following responses.
2 code implementations • 22 Nov 2023 • Yilun Liu, Shimin Tao, Xiaofeng Zhao, Ming Zhu, Wenbing Ma, Junhao Zhu, Chang Su, Yutai Hou, Miao Zhang, Min Zhang, Hongxia Ma, Li Zhang, Hao Yang, Yanfei Jiang
Instruction tuning is crucial for enabling Language Learning Models (LLMs) in responding to human instructions.
no code implementations • 20 Nov 2023 • Zhuocheng Zhang, Shuhao Gu, Min Zhang, Yang Feng
To solve the length bias problem, we propose to improve the DNMT model in training method, attention mechanism, and decoding strategy.
1 code implementation • 17 Nov 2023 • Shenghao Yang, Chenyang Wang, Yankai Liu, Kangping Xu, Weizhi Ma, Yiqun Liu, Min Zhang, Haitao Zeng, Junlan Feng, Chao Deng
In this paper, we propose CoWPiRec, an approach of Collaborative Word-based Pre-trained item representation for Recommendation.
1 code implementation • 16 Nov 2023 • Ziyi Ye, Qingyao Ai, Yiqun Liu, Maarten de Rijke, Min Zhang, Christina Lioma, Tuukka Ruotsalo
Inspired by recent research that revealed associations between the brain and the large computational language models, we propose a generative language BCI that utilizes the capacity of a large language model (LLM) jointly with a semantic brain decoder to directly generate language from functional magnetic resonance imaging (fMRI) input.
no code implementations • 15 Nov 2023 • Ziyang Chen, Dongfang Li, Xiang Zhao, Baotian Hu, Min Zhang
In this study, we address the challenge of enhancing temporal knowledge reasoning in Large Language Models (LLMs).
1 code implementation • 14 Nov 2023 • Zhenran Xu, Senbao Shi, Baotian Hu, Jindi Yu, Dongfang Li, Min Zhang, Yuxiang Wu
Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks.
1 code implementation • 14 Nov 2023 • Houquan Zhou, Yang Hou, Zhenghua Li, Xuebin Wang, Zhefeng Wang, Xinyu Duan, Min Zhang
While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or do they merely mimic comprehension through pattern recognition?
no code implementations • 13 Nov 2023 • Yunxin Li, Longyue Wang, Baotian Hu, Xinyu Chen, Wanqi Zhong, Chenyang Lyu, Wei Wang, Min Zhang
The emergence of multimodal large models (MLMs) has significantly advanced the field of visual understanding, offering remarkable capabilities in the realm of visual question answering (VQA).
1 code implementation • 13 Nov 2023 • Meizhi Zhong, Lemao Liu, Kehai Chen, Mingming Yang, Min Zhang
Simultaneous Machine Translation (SiMT) aims to yield a real-time partial translation with a monotonically growing the source-side context.
no code implementations • 10 Nov 2023 • Xilai Ma, Jing Li, Min Zhang
In this paper, we propose a novel approach for few-shot relation extraction using large language models, named CoT-ER, chain-of-thought with explicit evidence reasoning.
1 code implementation • 9 Nov 2023 • Tong Zhu, Junfei Ren, Zijian Yu, Mengsong Wu, Guoliang Zhang, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Min Zhang
Sharing knowledge between information extraction tasks has always been a challenge due to the diverse data formats and task variations.
1 code implementation • 7 Nov 2023 • Dongfang Li, Zetian Sun, Xinshuo Hu, Zhenyu Liu, Ziyang Chen, Baotian Hu, Aiguo Wu, Min Zhang
Open-domain generative systems have gained significant attention in the field of conversational AI (e. g., generative search engines).
1 code implementation • 5 Nov 2023 • Jianling Li, Meishan Zhang, Peiming Guo, Min Zhang, Yue Zhang
Our experimental results demonstrate that self-training for constituency parsing, equipped with an LLM, outperforms traditional methods regardless of the LLM's performance.
1 code implementation • 1 Nov 2023 • Weihang Su, Qingyao Ai, Yueyue Wu, Yixiao Ma, Haitao Li, Yiqun Liu, Zhijing Wu, Min Zhang
Legal case retrieval aims to help legal workers find relevant cases related to their cases at hand, which is important for the guarantee of fairness and justice in legal judgments.
no code implementations • 1 Nov 2023 • Mengxia Wu, Min Cao, Yang Bai, Ziyin Zeng, Chen Chen, Liqiang Nie, Min Zhang
In this paper, we make the first empirical study of frame selection for TVR.
1 code implementation • 23 Oct 2023 • Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang
In this paper, we propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally, and then dynamically influence the choice of the next token.
Ranked #1 on Grammatical Error Correction on MuCGEC
1 code implementation • 23 Oct 2023 • Zhengrui Ma, Shaolei Zhang, Shoutao Guo, Chenze Shao, Min Zhang, Yang Feng
Simultaneous machine translation (SiMT) models are trained to strike a balance between latency and translation quality.
1 code implementation • 20 Oct 2023 • Zecheng Tang, Kaifeng Qi, Juntao Li, Min Zhang
By leveraging the augmenting data from the GEC models themselves in the post-training process and introducing regularization data for cycle training, our proposed method can effectively improve the model robustness of well-trained GEC models with only a few more training epochs as an extra cost.
1 code implementation • 19 Oct 2023 • Zhenran Xu, Yulin Chen, Baotian Hu, Min Zhang
Zero-shot entity linking (EL) aims at aligning entity mentions to unseen entities to challenge the generalization ability.
1 code implementation • 19 Oct 2023 • Yulin Chen, Zhenran Xu, Baotian Hu, Min Zhang
Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base.
1 code implementation • 16 Oct 2023 • Haoke Zhang, Yue Wang, Juntao Li, Xiabing Zhou, Min Zhang
Large Language Models~(LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages.
1 code implementation • 12 Oct 2023 • Xin Zhang, Zehan Li, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang, Min Zhang
As such cases span from English to other natural or programming languages, from retrieval to classification and beyond, it is desirable to build a unified embedding model rather than dedicated ones for each scenario.
1 code implementation • 9 Oct 2023 • Hongqiu Wu, Linfeng Liu, Hai Zhao, Min Zhang
Beyond the great cognitive powers showcased by language models, it is crucial to scrutinize whether their reasoning capabilities stem from strong generalization or merely exposure to relevant data.
no code implementations • 7 Oct 2023 • Beining Wang, Ruizhe Zhang, Yueyue Wu, Qingyao Ai, Min Zhang, Yiqun Liu
Given a specific query case, legal case retrieval systems aim to retrieve a set of case documents relevant to the case at hand.
1 code implementation • 4 Oct 2023 • Murong Yue, Jie Zhao, Min Zhang, Liang Du, Ziyu Yao
Large language models (LLMs) such as GPT-4 have exhibited remarkable performance in a variety of tasks, but this strong performance often comes with the high expense of using paid API services.
1 code implementation • 19 Sep 2023 • Juntao Li, Zecheng Tang, Yuyang Ding, Pinzheng Wang, Pei Guo, Wangjie You, Dan Qiao, Wenliang Chen, Guohong Fu, Qiaoming Zhu, Guodong Zhou, Min Zhang
This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques.
no code implementations • 18 Sep 2023 • Yuang Li, Yinglu Li, Min Zhang, Chang Su, Mengxin Ren, Xiaosong Qiao, Xiaofeng Zhao, Mengyao Piao, Jiawei Yu, Xinglin Lv, Miaomiao Ma, Yanqing Zhao, Hao Yang
End-to-end automatic speech recognition (ASR) systems often struggle to recognize rare name entities, such as personal names, organizations, and terminologies not frequently encountered in the training data.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
1 code implementation • 11 Sep 2023 • Linhan Wang, Shuo Lei, Jianfeng He, Shengkun Wang, Min Zhang, Chang-Tien Lu
To tackle these challenges, we propose a Self-Correlation and Cross-Correlation Learning Network for the few-shot remote sensing image semantic segmentation.
no code implementations • 24 Aug 2023 • Yue Wang, Xinrui Wang, Juntao Li, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Min Zhang
Instruction tuning is instrumental in enabling Large Language Models~(LLMs) to follow user instructions to complete various open-domain tasks.
1 code implementation • 19 Aug 2023 • Min Cao, Yang Bai, Ziyin Zeng, Mang Ye, Min Zhang
TPBS, as a fine-grained cross-modal retrieval task, is also facing the rise of research on the CLIP-based TBPS.
Ranked #4 on Text based Person Retrieval on RSTPReid
1 code implementation • 16 Aug 2023 • Xinshuo Hu, Dongfang Li, Baotian Hu, Zihao Zheng, Zhenyu Liu, Min Zhang
To evaluate the effectiveness of our approach in terms of truthfulness and detoxification, we conduct extensive experiments on LLMs, encompassing additional abilities such as language modeling and mathematical reasoning.
2 code implementations • 16 Aug 2023 • Zecheng Tang, Keyan Zhou, Juntao Li, Yuyang Ding, Pinzheng Wang, Bowen Yan, Min Zhang
In view of this, we introduce a Context-aware Model self-Detoxification~(CMD) framework that pays attention to both the context and the detoxification process, i. e., first detoxifying the context and then making the language model generate along the safe context.
no code implementations • 13 Aug 2023 • Weishan Ye, Zhiguo Zhang, Min Zhang, Fei Teng, Li Zhang, Linling Li, Gan Huang, Jianhong Wang, Dong Ni, Zhen Liang
In this paper, a semi-supervised Dual-stream Self-Attentive Adversarial Graph Contrastive learning framework (termed as DS-AGC) is proposed to tackle the challenge of limited labeled data in cross-subject EEG-based emotion recognition.
no code implementations • 9 Aug 2023 • Yu Zhao, Hao Fei, Yixin Cao, Bobo Li, Meishan Zhang, Jianguo Wei, Min Zhang, Tat-Seng Chua
A scene-event mapping mechanism is first designed to bridge the gap between the underlying scene structure and the high-level event semantic structure, resulting in an overall hierarchical scene-event (termed ICE) graph structure.
no code implementations • 3 Aug 2023 • Hao Fei, Meishan Zhang, Min Zhang, Tat-Seng Chua
Structured Natural Language Processing (XNLP) is an important subset of NLP that entails understanding the underlying semantic or syntactic structure of texts, which serves as a foundational component for many downstream applications.
1 code implementation • 25 Jul 2023 • Hexuan Deng, Xin Zhang, Meishan Zhang, Xuebo Liu, Min Zhang
In this paper, we conduct a holistic exploration of the Universal Decompositional Semantic (UDS) Parsing.
1 code implementation • 24 Jul 2023 • Yifan Wang, Peijie Sun, Min Zhang, Qinglin Jia, Jingjie Li, Shaoping Ma
To directly introduce the correct feedback label information, we propose an Unbiased delayed feedback Label Correction framework (ULC), which uses an auxiliary model to correct labels for observed negative feedback samples.
no code implementations • 19 Jul 2023 • Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, Shen Gao, Jiafeng Guo, Xiangnan He, Yanyan Lan, Chenliang Li, Yiqun Liu, Ziyu Lyu, Weizhi Ma, Jun Ma, Zhaochun Ren, Pengjie Ren, Zhiqiang Wang, Mingwen Wang, Ji-Rong Wen, Le Wu, Xin Xin, Jun Xu, Dawei Yin, Peng Zhang, Fan Zhang, Weinan Zhang, Min Zhang, Xiaofei Zhu
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
1 code implementation • 17 Jul 2023 • Jiayin Wang, Weizhi Ma, Chumeng Jiang, Min Zhang, Yuan Zhang, Biao Li, Peng Jiang
In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items.
no code implementations • 12 Jul 2023 • Xuewei Wang, Qiang Jin, Shengyu Huang, Min Zhang, Xi Liu, Zhengli Zhao, Yukun Chen, Zhengyu Zhang, Jiyan Yang, Ellie Wen, Sagar Chordia, Wenlin Chen, Qin Huang
In order to pass better ads from the early to the final stage ranking, we propose a multi-task learning framework for early stage ranking to capture multiple final stage ranking components (i. e. ads clicks and ads quality events) and their task relations.
no code implementations • 1 Jul 2023 • Weihang Su, Xiangsheng Li, Yiqun Liu, Min Zhang, Shaoping Ma
Our team(THUIR2) participated in both FOSS and POSS subtasks of the NTCIR-161 Session Search (SS) Task.
no code implementations • 28 Jun 2023 • Didi Zhu, Zexi Li, Min Zhang, Junkun Yuan, Yunfeng Shao, Jiashuo Liu, Kun Kuang, Yinchuan Li, Chao Wu
It is found that NC optimality of text-to-image representations shows a positive correlation with downstream generalizability, which is more severe under class imbalance settings.
1 code implementation • 22 Jun 2023 • Senbao Shi, Zhenran Xu, Baotian Hu, Min Zhang
Multimodal Entity Linking (MEL) is the task of mapping mentions with multimodal contexts to the referent entities from a knowledge base.
no code implementations • ICCV 2023 • Jingwen Guo, Hong Liu, Shitong Sun, Tianyu Guo, Min Zhang, Chenyang Si
Existing skeleton-based action recognition methods typically follow a centralized learning paradigm, which can pose privacy concerns when exposing human-related videos.
no code implementations • 15 Jun 2023 • Jing Li, Yequan Wang, Shuai Zhang, Min Zhang
Recently, numerous efforts have continued to push up performance boundaries of document-level relation extraction (DocRE) and have claimed significant progress in DocRE.
no code implementations • 13 Jun 2023 • Hao Yang, Min Zhang, Shimin Tao, Minghan Wang, Daimeng Wei, Yanfei Jiang
Cross-lingual Machine Translation (MT) quality estimation plays a crucial role in evaluating translation performance.
no code implementations • 5 Jun 2023 • Wenwen Yu, Chengquan Zhang, Haoyu Cao, Wei Hua, Bohan Li, Huang Chen, MingYu Liu, Mingrui Chen, Jianfeng Kuang, Mengjun Cheng, Yuning Du, Shikun Feng, Xiaoguang Hu, Pengyuan Lyu, Kun Yao, Yuechen Yu, Yuliang Liu, Wanxiang Che, Errui Ding, Cheng-Lin Liu, Jiebo Luo, Shuicheng Yan, Min Zhang, Dimosthenis Karatzas, Xing Sun, Jingdong Wang, Xiang Bai
It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.
no code implementations • 27 May 2023 • Jinpeng Zhang, Nini Xiao, Ke Wang, Chuanqi Dong, Xiangyu Duan, Yuqi Zhang, Min Zhang
Lexically constrained neural machine translation (LCNMT), which controls the translation generation with pre-specified constraints, is important in many practical applications.
no code implementations • 26 May 2023 • Yingjie Feng, Jun Wang, Xianfeng GU, Xiaoyin Xu, Min Zhang
In diagnosing challenging conditions such as Alzheimer's disease (AD), imaging is an important reference.
no code implementations • 26 May 2023 • Zhiyi Xue, Si Liu, Zhaodi Zhang, Yiting Wu, Min Zhang
In this paper, we study existing approaches and identify a dominant factor in defining tight approximation, namely the approximation domain of the activation function.
1 code implementation • 26 May 2023 • Zhiwei Cao, Baosong Yang, Huan Lin, Suhang Wu, Xiangpeng Wei, Dayiheng Liu, Jun Xie, Min Zhang, Jinsong Su
$k$-Nearest neighbor machine translation ($k$NN-MT) has attracted increasing attention due to its ability to non-parametrically adapt to new translation domains.
no code implementations • 25 May 2023 • Yunze Tong, Junkun Yuan, Min Zhang, Didi Zhu, Keli Zhang, Fei Wu, Kun Kuang
With contrastive learning, we propose a learning potential-guided metric for domain heterogeneity by promoting learning variant features.
1 code implementation • 25 May 2023 • Yue Zhang, Bo Zhang, Haochen Jiang, Zhenghua Li, Chen Li, Fei Huang, Min Zhang
We introduce NaSGEC, a new dataset to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains.
1 code implementation • 24 May 2023 • Qihuang Zhong, Liang Ding, Juhua Liu, Xuebo Liu, Min Zhang, Bo Du, DaCheng Tao
Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT, by skipping the computation of a subset of the input tokens at several middle layers.
1 code implementation • 23 May 2023 • Yang Bai, Min Cao, Daming Gao, Ziqiang Cao, Chen Chen, Zhenfeng Fan, Liqiang Nie, Min Zhang
RA offsets the overfitting risk by introducing a novel positive relation detection task (i. e., learning to distinguish strong and weak positive pairs).
Ranked #2 on Text based Person Retrieval on RSTPReid
no code implementations • 22 May 2023 • Yang Bai, Jingyao Wang, Min Cao, Chen Chen, Ziqiang Cao, Liqiang Nie, Min Zhang
Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description.
1 code implementation • 22 May 2023 • Dongfang Li, Jindi Yu, Baotian Hu, Zhenran Xu, Min Zhang
As ChatGPT and GPT-4 spearhead the development of Large Language Models (LLMs), more researchers are investigating their performance across various tasks.
1 code implementation • 22 May 2023 • Shilin Zhou, Zhenghua Li, Yu Hong, Min Zhang, Zhefeng Wang, Baoxing Huai
Previous approaches have attempted to address this by utilizing the NE dictionary.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 21 May 2023 • Gongyao Jiang, Shuang Liu, Meishan Zhang, Min Zhang
Dialogue-level dependency parsing has received insufficient attention, especially for Chinese.
no code implementations • 20 May 2023 • Hao Fei, Meishan Zhang, Min Zhang, Tat-Seng Chua
Latest efforts on cross-lingual relation extraction (XRE) aggressively leverage the language-consistent structural features from the universal dependency (UD) resource, while they may largely suffer from biased transfer (e. g., either target-biased or source-biased) due to the inevitable linguistic disparity between languages.
1 code implementation • 20 May 2023 • Hao Fei, Qian Liu, Meishan Zhang, Min Zhang, Tat-Seng Chua
In this work, we investigate a more realistic unsupervised multimodal machine translation (UMMT) setup, inference-time image-free UMMT, where the model is trained with source-text image pairs, and tested with only source-text inputs.
1 code implementation • 19 May 2023 • Yu Zhao, Hao Fei, Wei Ji, Jianguo Wei, Meishan Zhang, Min Zhang, Tat-Seng Chua
With an external 3D scene extractor, we obtain the 3D objects and scene features for input images, based on which we construct a target object-centered 3D spatial scene graph (Go3D-S2G), such that we model the spatial semantics of target objects within the holistic 3D scenes.
1 code implementation • 8 May 2023 • Yunxin Li, Baotian Hu, Xinyu Chen, Yuxin Ding, Lin Ma, Min Zhang
This makes the language model well-suitable for such multi-modal reasoning scenario on joint textual and visual clues.
1 code implementation • 8 May 2023 • Hongqiu Wu, Yongxiang Liu, Hanwen Shi, Hai Zhao, Min Zhang
Based on the observation, we propose simple yet effective \textit{Contextualized representation-Adversarial Training} (CreAT), in which the attack is explicitly optimized to deviate the contextualized representation of the encoder.
1 code implementation • 8 May 2023 • Zecheng Tang, Pinzheng Wang, Keyan Zhou, Juntao Li, Ziqiang Cao, Min Zhang
Diffusion models have been successfully adapted to text generation tasks by mapping the discrete text into the continuous space.
1 code implementation • 5 May 2023 • Yunxin Li, Baotian Hu, Xinyu Chen, Lin Ma, Yong Xu, Min Zhang
LMEye addresses this issue by allowing the LLM to request the desired visual information aligned with various human instructions, which we term as the dynamic visual information interaction.
1 code implementation • 3 May 2023 • Yunxin Li, Baotian Hu, Yuxin Ding, Lin Ma, Min Zhang
Inspired by the Divide-and-Conquer algorithm and dual-process theory, in this paper, we regard linguistically complex texts as compound proposition texts composed of multiple simple proposition sentences and propose an end-to-end Neural Divide-and-Conquer Reasoning framework, dubbed NDCR.
no code implementations • 25 Apr 2023 • Yi Su, Yixin Ji, Juntao Li, Hai Ye, Min Zhang
Accordingly, in this paper, we propose perturbation consistency learning (PCL), a simple test-time adaptation method to promote the model to make stable predictions for samples with distribution shifts.
2 code implementations • 15 Apr 2023 • Jiayu Li, Peijie Sun, Zhefan Wang, Weizhi Ma, Yangkun Li, Min Zhang, Zhoutian Feng, Daiyue Xue
To address such a task, we propose an Intent-aware ranking Ensemble Learning~(IntEL) model to fuse multiple single-objective item lists with various user intents, in which item-level personalized weights are learned.
1 code implementation • 13 Apr 2023 • Hao Fei, Shengqiong Wu, Jingye Li, Bobo Li, Fei Li, Libo Qin, Meishan Zhang, Min Zhang, Tat-Seng Chua
Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM.
no code implementations • 30 Mar 2023 • Chengliang Liu, Jie Wen, Yong Xu, Liqiang Nie, Min Zhang
The application of multi-view contrastive learning has further facilitated this process, however, the existing multi-view contrastive learning methods crudely separate the so-called negative pair, which largely results in the separation of samples belonging to the same category or similar ones.
1 code implementation • 27 Mar 2023 • Siteng Huang, Biao Gong, Yutong Feng, Min Zhang, Yiliang Lv, Donglin Wang
Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs.
1 code implementation • 24 Mar 2023 • Keqin Peng, Liang Ding, Qihuang Zhong, Li Shen, Xuebo Liu, Min Zhang, Yuanxin Ouyang, DaCheng Tao
We show that: 1) The performance of ChatGPT depends largely on temperature, and a lower temperature usually can achieve better performance; 2) Emphasizing the task information can further improve ChatGPT's performance, particularly in complex MT tasks; 3) Introducing domain information can elicit ChatGPT's generalization ability and improve its performance in the specific domain; 4) ChatGPT tends to generate hallucinations for non-English-centric MT tasks, which can be partially addressed by our proposed prompts but still need to be highlighted for the MT/NLP community.
1 code implementation • CVPR 2023 • Zhaodi Zhang, Zhiyi Xue, Yang Chen, Si Liu, Yueling Zhang, Jing Liu, Min Zhang
Via abstraction, all perturbed images are mapped into intervals before feeding into neural networks for training.
no code implementations • 20 Mar 2023 • Rongxiang Weng, Qiang Wang, Wensen Cheng, Changfeng Zhu, Min Zhang
A contributing factor to this problem is that NMT models trained with the one-to-one paradigm struggle to handle the source diversity phenomenon, where inputs with the same meaning can be expressed differently.
no code implementations • 20 Mar 2023 • Min Zhang, Jintang Xue, Pranav Kadam, Hardik Prajapati, Shan Liu, C. -C. Jay Kuo
On the other hand, the model size and inference complexity of DGCNN are 42X and 1203X of those of Green-PointHop, respectively.
no code implementations • 14 Mar 2023 • Pei Guo, Yisheng Xiao, Juntao Li, Min Zhang
Non-autoregressive neural machine translation (NAT) models are proposed to accelerate the inference process while maintaining relatively high performance.
no code implementations • 14 Mar 2023 • Min Cao, Yang Bai, Jingyao Wang, Ziqiang Cao, Liqiang Nie, Min Zhang
The proposed framework equipped with only two embedding layers achieves $O(1)$ querying time complexity, while improving the retrieval efficiency and keeping its performance, when applied prior to the common image-text retrieval methods.
1 code implementation • 13 Mar 2023 • Yisheng Xiao, Ruiyang Xu, Lijun Wu, Juntao Li, Tao Qin, Yan-Tie Liu, Min Zhang
Experiments on \textbf{3} different tasks (neural machine translation, summarization, and code generation) with \textbf{15} datasets in total confirm that our proposed simple method achieves significant performance improvement over the strong CMLM model.
no code implementations • 12 Mar 2023 • Min Zhang, Zifeng Zhuang, Zhitao Wang, Donglin Wang, Wenbin Li
OOD exacerbates inconsistencies in magnitudes and directions of task gradients, which brings challenges for GBML to optimize the meta-knowledge by minimizing the sum of task gradients in each minibatch.
1 code implementation • 12 Mar 2023 • Zhengrui Ma, Chenze Shao, Shangtong Gui, Min Zhang, Yang Feng
Non-autoregressive translation (NAT) reduces the decoding latency but suffers from performance degradation due to the multi-modality problem.
no code implementations • 2 Mar 2023 • Hongyao Tang, Min Zhang, Jianye Hao
On typical MuJoCo and DeepMind Control Suite (DMC) benchmarks, we find common phenomena for TD3 and RAD agents: 1) the activity of policy network parameters is highly asymmetric and policy networks advance monotonically along very few major parameter directions; 2) severe detours occur in parameter update and harmonic-like changes are observed for all minor parameter directions.
no code implementations • 22 Feb 2023 • Pranav Kadam, Hardik Prajapati, Min Zhang, Jintang Xue, Shan Liu, C. -C. Jay Kuo
Many point cloud classification methods are developed under the assumption that all point clouds in the dataset are well aligned with the canonical axes so that the 3D Cartesian point coordinates can be employed to learn features.
no code implementations • 30 Jan 2023 • Zhanglin Wu, Min Zhang, Ming Zhu, Yinglu Li, Ting Zhu, Hao Yang, Song Peng, Ying Qin
BERTScore is an effective and robust automatic metric for referencebased machine translation evaluation.
no code implementations • 27 Jan 2023 • Chulun Zhou, Yunlong Liang, Fandong Meng, Jie zhou, Jinan Xu, Hongji Wang, Min Zhang, Jinsong Su
To address these issues, in this paper, we propose a multi-task multi-stage transitional (MMT) training framework, where an NCT model is trained using the bilingual chat translation dataset and additional monolingual dialogues.
no code implementations • 27 Jan 2023 • Xingwu Guo, Ziwei Zhou, Yueling Zhang, Guy Katz, Min Zhang
The experimental results demonstrate our approach's effectiveness and efficiency in verifying DNNs' robustness against various occlusions, and its ability to generate counterexamples when these DNNs are not robust.
1 code implementation • ICCV 2023 • Min Zhang, Junkun Yuan, Yue He, Wenbin Li, Zhengyu Chen, Kun Kuang
To achieve this goal, we apply a bilevel optimization to explicitly model and optimize the coupling relationship between the OOD model and auxiliary adapter layers.
1 code implementation • 22 Dec 2022 • Zhaoxin Fan, Kaixing Yang, Min Zhang, Zhenbo Song, Hongyan Liu, Jun He
In stage 1, a novel devices detection and tracking scheme is introduced, which accurately locate the height limit devices in the left or right image.
1 code implementation • 16 Dec 2022 • Qian Yang, Qian Chen, Wen Wang, Baotian Hu, Min Zhang
Moreover, the pipelined approaches of retrieval and generation might result in poor generation performance when retrieval performance is low.
no code implementations • 12 Dec 2022 • Yachao Li, Junhui Li, Jing Jiang, Shimin Tao, Hao Yang, Min Zhang
To alleviate this problem, we propose a position-aware Transformer (P-Transformer) to enhance both the absolute and relative position information in both self-attention and cross-attention.
1 code implementation • 10 Dec 2022 • Yedi Zhang, Zhe Zhao, Fu Song, Min Zhang, Taolue Chen, Jun Sun
Experimental results on QNNs with different quantization bits confirm the effectiveness and efficiency of our approach, e. g., two orders of magnitude faster and able to solve more verification tasks in the same time limit than the state-of-the-art methods.
1 code implementation • 8 Dec 2022 • Zhaocong Li, Xuebo Liu, Derek F. Wong, Lidia S. Chao, Min Zhang
In this paper, we propose a novel transfer learning method for NMT, namely ConsistTL, which can continuously transfer knowledge from the parent model during the training of the child model.
1 code implementation • 2 Dec 2022 • Hexuan Deng, Liang Ding, Xuebo Liu, Meishan Zhang, DaCheng Tao, Min Zhang
Preliminary experiments on En-Zh and En-Ja news domain corpora demonstrate that monolingual data can significantly improve translation quality (e. g., +3. 15 BLEU on En-Zh).
1 code implementation • 23 Nov 2022 • Zhijun Wang, Xuebo Liu, Min Zhang
Existing research generally treats Chinese character as a minimum unit for representation.
Ranked #1 on Machine Translation on WMT2017 Chinese-English
no code implementations • 21 Nov 2022 • Jiaxu Tian, Dapeng Zhi, Si Liu, Peixin Wang, Guy Katz, Min Zhang
The experimental results on a wide range of benchmarks show that the DNNs trained by using our approach exhibit comparable performance, while the reachability analysis of the corresponding systems becomes more amenable with significant tightness and efficiency improvement over the state-of-the-art white-box approaches.
no code implementations • 21 Nov 2022 • Yiting Wu, Zhaodi Zhang, Zhiyi Xue, Si Liu, Min Zhang
We observe that existing approaches only rely on overestimated domains, while the corresponding tight approximation may not necessarily be tight on its actual domain.
1 code implementation • 13 Nov 2022 • Binbin Xie, Xiangpeng Wei, Baosong Yang, Huan Lin, Jun Xie, Xiaoli Wang, Min Zhang, Jinsong Su
Keyphrase generation aims to automatically generate short phrases summarizing an input document.
1 code implementation • 8 Nov 2022 • Jinpeng Zhang, Chuanqi Dong, Xiangyu Duan, Yuqi Zhang, Min Zhang
Word alignment is to find translationally equivalent words between source and target sentences.