1 code implementation • NAACL 2022 • Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao
To inherit the advantages of these two types of methods, we propose OPERA, an operation-pivoted discrete reasoning framework, where lightweight symbolic operations (compared with logical forms) as neural modules are utilized to facilitate the reasoning ability and interpretability.
no code implementations • CCL 2022 • Jiarui Tang, Liu Meiling, Tiejun Zhao, Jiyun Zhou
“神经网络模型的快速发展使得多文档摘要可以获得人类可读的流畅的摘要, 对大规模的数据进行预训练可以更好的从自然语言文本中捕捉更丰富的语义信息, 并更好的作用于下游任务。目前很多的多文档摘要的工作也应用了预训练模型(如BERT)并取得了一定的效果, 但是这些预训练模型不能更好的从文本中捕获事实性知识, 没有考虑到多文档文本的结构化的实体-关系信息, 本文提出了基于实体信息增强和多粒度融合的多文档摘要模型MGNIE, 将实体关系信息融入预训练模型ERNIE中, 增强知识事实以获得多层语义信息, 解决摘要生成的事实一致性问题。进而从多种粒度进行多文档层次结构的融合建模, 以词信息、实体信息以及句子信息捕捉长文本信息摘要生成所需的关键信息点。本文设计的模型, 在国际标准评测数据集MultiNews上对比强基线模型效果和竞争力获得较大提升。”
1 code implementation • NAACL 2022 • Tingting Ma, Qianhui Wu, Zhiwei Yu, Tiejun Zhao, Chin-Yew Lin
Recent studies on few-shot intent detection have attempted to formulate the task as a meta-learning problem, where a meta-learning model is trained with a certain capability to quickly adapt to newly specified few-shot tasks with potentially unseen intent categories.
no code implementations • COLING 2022 • Zihao Feng, Hailong Cao, Tiejun Zhao, Weixuan Wang, Wei Peng
Despite their progress in high-resource language settings, unsupervised bilingual lexicon induction (UBLI) models often fail on corpora with low-resource distant language pairs due to insufficient initialization.
no code implementations • Findings (NAACL) 2022 • Wang Xu, Tiejun Zhao
Abstractive summarization can generate high quality results with the development of the neural network.
no code implementations • CCL 2020 • Ting Jiang, Bing Xu, Tiejun Zhao, Sheng Li
In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN).
no code implementations • 29 May 2025 • Qiuyu Ding, Zhiqiang Cao, Hailong Cao, Tiejun Zhao
Large language models have demonstrated exceptional performance across multiple crosslingual NLP tasks, including machine translation (MT).
no code implementations • 29 May 2025 • Qiuyu Ding, Zhiqiang Cao, Hailong Cao, Tiejun Zhao
Bilingual Lexicon Induction (BLI) is generally based on common domain data to obtain monolingual word embedding, and by aligning the monolingual word embeddings to obtain the cross-lingual embeddings which are used to get the word translation pairs.
1 code implementation • 28 May 2025 • Yudi Zhang, Weilin Zhao, Xu Han, Tiejun Zhao, Wang Xu, Hailong Cao, Conghui Zhu
Surprisingly, experiments applying the advanced speculative decoding method EAGLE-2 to various quantized models reveal that the memory benefits from 4-bit weight quantization are diminished by the computational load from speculative decoding.
1 code implementation • 21 May 2025 • Hongli Zhou, Hui Huang, Ziqing Zhao, Lvyuan Han, Huicheng Wang, Kehai Chen, Muyun Yang, Wei Bao, Jian Dong, Bing Xu, Conghui Zhu, Hailong Cao, Tiejun Zhao
The evaluation of large language models (LLMs) via benchmarks is widespread, yet inconsistencies between different leaderboards and poor separability among top models raise concerns about their ability to accurately reflect authentic model capabilities.
1 code implementation • 17 Feb 2025 • Hui Huang, Jiaheng Liu, Yancheng He, Shilong Li, Bing Xu, Conghui Zhu, Muyun Yang, Tiejun Zhao
Complex instruction-following with elaborate constraints is imperative for Large Language Models (LLMs).
no code implementations • 17 Feb 2025 • Andong Chen, Yuchen Song, Wenxin Zhu, Kehai Chen, Muyun Yang, Tiejun Zhao, Min Zhang
The o1-Like LLMs are transforming AI by simulating human cognitive processes, but their performance in multilingual machine translation (MMT) remains underexplored.
1 code implementation • 16 Feb 2025 • Xiangyu Lu, Wang Xu, Haoyu Wang, Hongyun Zhou, Haiyan Zhao, Conghui Zhu, Tiejun Zhao, Muyun Yang
In this paper, we propose DuplexMamba, a Mamba-based end-to-end multimodal duplex model for speech-to-text conversation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
no code implementations • 17 Dec 2024 • Andong Chen, Yuchen Song, Kehai Chen, Muyun Yang, Tiejun Zhao, Min Zhang
Visual information has been introduced for enhancing machine translation (MT), and its effectiveness heavily relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations.
no code implementations • 17 Dec 2024 • Mufan Xu, Kehai Chen, Xuefeng Bai, Muyun Yang, Tiejun Zhao, Min Zhang
Large language models (LLMs) based on generative pre-trained Transformer have achieved remarkable performance on knowledge graph question-answering (KGQA) tasks.
1 code implementation • 10 Dec 2024 • Shaoqing Zhang, Zhuosheng Zhang, Kehai Chen, Rongxiang Weng, Muyun Yang, Tiejun Zhao, Min Zhang
This vulnerability poses significant risks to the real-world applications.
no code implementations • 2 Nov 2024 • Dongxu Liu, Bing Xu, Yinzhuo Chen, Bufan Xu, Wenpeng Lu, Muyun Yang, Tiejun Zhao
Reinforcement Learning from Human Feedback (RLHF) has been proven to be an effective method for preference alignment of large language models (LLMs) and is widely used in the post-training process of LLMs.
no code implementations • 16 Oct 2024 • Andong Chen, Kehai Chen, Yang Xiang, Xuefeng Bai, Muyun Yang, Yang Feng, Tiejun Zhao, Min Zhang
The remarkable understanding and generation capabilities of large language models (LLMs) have greatly improved translation performance.
1 code implementation • 1 Oct 2024 • Shaoqing Zhang, Zhuosheng Zhang, Kehai Chen, Xinbei Ma, Muyun Yang, Tiejun Zhao, Min Zhang
However, a key challenge lies in devising effective plans to guide action prediction in GUI tasks, though planning have been widely recognized as effective for decomposing complex tasks into a series of steps.
1 code implementation • 25 Sep 2024 • Hongli Zhou, Hui Huang, Yunfei Long, Bing Xu, Conghui Zhu, Hailong Cao, Muyun Yang, Tiejun Zhao
Recently, there has been a trend of evaluating the Large Language Model (LLM) quality in the flavor of LLM-as-a-Judge, namely leveraging another LLM to evaluate the current output quality.
no code implementations • 19 Aug 2024 • Andong Chen, Lianzhang Lou, Kehai Chen, Xuefeng Bai, Yang Xiang, Muyun Yang, Tiejun Zhao, Min Zhang
Different from the traditional translation tasks, classical Chinese poetry translation requires both adequacy and fluency in translating culturally and historically significant content and linguistic poetic elegance.
no code implementations • 17 Jun 2024 • Ruili Jiang, Kehai Chen, Xuefeng Bai, Zhixuan He, Juntao Li, Muyun Yang, Tiejun Zhao, Liqiang Nie, Min Zhang
In this survey, we review the progress in exploring human preference learning for LLMs from a preference-centered perspective, covering the sources and formats of preference feedback, the modeling and usage of preference signals, as well as the evaluation of the aligned LLMs.
1 code implementation • 16 Jun 2024 • Xiaoxiao Ma, Mohan Zhou, Tao Liang, Yalong Bai, Tiejun Zhao, Huaian Chen, Yi Jin
We present STAR, a text-to-image model that employs scale-wise auto-regressive paradigm.
1 code implementation • 11 Jun 2024 • Andong Chen, Lianzhang Lou, Kehai Chen, Xuefeng Bai, Yang Xiang, Muyun Yang, Tiejun Zhao, Min Zhang
Recently, large language models (LLMs) enhanced by self-reflection have achieved promising performance on machine translation.
no code implementations • 23 Apr 2024 • Jieru Lin, Danqing Huang, Tiejun Zhao, Dechen Zhan, Chin-Yew Lin
This complexity makes the comprehension of graphic design challenging, for it needs the capability to both recognize the design elements and understand the design.
no code implementations • 27 Mar 2024 • Yongwei Zhou, Tiejun Zhao
To alleviate this problem, we propose a dual instruction tuning strategy to meticulously model mathematical reasoning from both forward and reverse directions.
1 code implementation • 7 Mar 2024 • Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao, Wenpeng Lu
The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods.
1 code implementation • 5 Mar 2024 • Hui Huang, Yingqi Qu, Xingyuan Bu, Hongli Zhou, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao
Alternatively, other works have fine-tuned judge models based on open-source LLMs as the evaluator.
1 code implementation • IEEE/ACM Transactions on Audio, Speech, and Language Processing (Volume: 32) 2024 • Tingting Ma, Qianhui Wu, Huiqiang Jiang, Jieru Lin, Börje F. Karlsson, Tiejun Zhao, Chin-Yew Lin
With the detected mention spans, we further leverage the MAML-enhanced span-level prototypical network for few-shot type classification.
Ranked #1 on
Slot Filling
on SNIPS
(F1 (1-shot) avg metric)
no code implementations • 12 Feb 2024 • Hongyun Zhou, Xiangyu Lu, Wang Xu, Conghui Zhu, Tiejun Zhao, Muyun Yang
In fact, the output of LoRA (product of LoRA parameter and hidden state), directly impacts the final results.
1 code implementation • 29 Jan 2024 • Jieru Lin, Danqing Huang, Tiejun Zhao, Dechen Zhan, Chin-Yew Lin
Furthermore, based on our observation that pixel space is more sensitive in capturing spatial patterns of graphic layouts (e. g., overlap, alignment), we propose a learning-based locator to detect erroneous tokens which takes the wireframe image rendered from the generated layout sequence as input.
no code implementations • 25 Jan 2024 • Mohan Zhou, Yalong Bai, Qing Yang, Tiejun Zhao
The ability to fine-tune generative models for text-to-image generation tasks is crucial, particularly facing the complexity involved in accurately interpreting and visualizing textual inputs.
no code implementations • 22 Aug 2023 • Yingyao Wang, Yongwei Zhou, Chaoqun Duan, Junwei Bao, Tiejun Zhao
To alleviate these challenges, we propose a self-iterative framework for multi-hop program generation (HopPG) over heterogeneous knowledge, which leverages the previous execution results to retrieve supporting facts and generate subsequent programs hop by hop.
no code implementations • 20 Jul 2023 • Mohan Zhou, Yalong Bai, Wei zhang, Ting Yao, Tiejun Zhao, Tao Mei
In this paper, we propose a novel learning-based evaluation metric named Preference Score (PS) for fitting human preference according to the quantitative evaluations across different dimensions.
no code implementations • 5 Jul 2023 • Mohan Zhou, Yalong Bai, Wei zhang, Ting Yao, Tiejun Zhao
Based on ViCo and ViCo-X, we define three novel tasks targeting the interaction modeling during the face-to-face conversation: 1) responsive listening head generation making listeners respond actively to the speaker with non-verbal signals, 2) expressive talking head generation guiding speakers to be aware of listeners' behaviors, and 3) conversational head generation to integrate the talking/listening ability in one interlocutor.
no code implementations • 21 Jun 2023 • Mohan Zhou, Yalong Bai, Wei zhang, Ting Yao, Tiejun Zhao, Tao Mei
Dynamically synthesizing talking speech that actively responds to a listening head is critical during the face-to-face interaction.
1 code implementation • 24 May 2023 • Tingting Ma, Qianhui Wu, Huiqiang Jiang, Börje F. Karlsson, Tiejun Zhao, Chin-Yew Lin
Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language.
no code implementations • 20 Oct 2022 • Wanjun Zhong, Tingting Ma, Jiahai Wang, Jian Yin, Tiejun Zhao, Chin-Yew Lin, Nan Duan
This paper presents ReasonFormer, a unified reasoning framework for mirroring the modular and compositional reasoning process of humans in complex decision-making.
1 code implementation • 19 Oct 2022 • Yingyao Wang, Junwei Bao, Chaoqun Duan, Youzheng Wu, Xiaodong He, Tiejun Zhao
To preserve the advantage and eliminate the disadvantage of different granularity evidence, we propose MuGER$^2$, a Multi-Granularity Evidence Retrieval and Reasoning approach.
1 code implementation • 15 Oct 2022 • Yongwei Zhou, Junwei Bao, Chaoqun Duan, Youzheng Wu, Xiaodong He, Tiejun Zhao
Question answering requiring discrete reasoning, e. g., arithmetic computing, comparison, and counting, over knowledge is a challenging task.
no code implementations • 29 Apr 2022 • Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao
To inherit the advantages of these two types of methods, we propose OPERA, an operation-pivoted discrete reasoning framework, where lightweight symbolic operations (compared with logical forms) as neural modules are utilized to facilitate the reasoning ability and interpretability.
1 code implementation • NAACL 2022 • Wang Xu, Kehai Chen, Lili Mou, Tiejun Zhao
Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences.
Ranked #5 on
Dialog Relation Extraction
on DialogRE
(F1c (v1) metric)
Dialog Relation Extraction
Document-level Relation Extraction
+2
1 code implementation • Findings (ACL) 2022 • Tingting Ma, Huiqiang Jiang, Qianhui Wu, Tiejun Zhao, Chin-Yew Lin
Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples.
Ranked #6 on
Few-shot NER
on Few-NERD (INTER)
1 code implementation • Findings (NAACL) 2022 • Zhen Li, Bing Xu, Conghui Zhu, Tiejun Zhao
Compared with unimodal data, multimodal data can provide more features to help the model analyze the sentiment of data.
no code implementations • 27 Dec 2021 • Mohan Zhou, Yalong Bai, Wei zhang, Ting Yao, Tiejun Zhao, Tao Mei
Automatically synthesizing listening behavior that actively responds to a talking head, is critical to applications such as digital human, virtual agents and social robots.
1 code implementation • 20 Aug 2021 • Changzhen Ji, Yating Zhang, Xiaozhong Liu, Adam Jatowt, Changlong Sun, Conghui Zhu, Tiejun Zhao
Nevertheless, few works utilized the knowledge extracted from similar conversations for utterance generation.
1 code implementation • 18 Aug 2021 • Yongwei Zhou, Junwei Bao, Haipeng Sun, Jiahui Liang, Youzheng Wu, Xiaodong He, BoWen Zhou, Tiejun Zhao
Reasoning machine reading comprehension (R-MRC) aims to answer complex questions that require discrete reasoning based on text.
1 code implementation • ACL 2021 • Tingting Ma, Jin-Ge Yao, Chin-Yew Lin, Tiejun Zhao
The general format of natural language inference (NLI) makes it tempting to be used for zero-shot text classification by casting any target label into a sentence of hypothesis and verifying whether or not it could be entailed by the input, aiming at generic classification applicable on any specified label space.
no code implementations • SEMEVAL 2021 • Chenyi Wang, Tianshu Liu, Tiejun Zhao
This paper introduces our system at SemEval-2021 Task 5: Toxic Spans Detection.
2 code implementations • Findings (ACL) 2021 • Wang Xu, Kehai Chen, Tiejun Zhao
Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i. e., pattern recognition, logical reasoning, coreference reasoning, etc.)
Ranked #24 on
Relation Extraction
on DocRED
no code implementations • 26 May 2021 • Hailong Cao, Tiejun Zhao
Embeddings of two languages are made to match with each other by rotating and scaling.
no code implementations • 16 Mar 2021 • Pengbo Liu, Hailong Cao, Tiejun Zhao
Multi-modal machine translation (MMT) improves translation quality by introducing visual information.
Ranked #5 on
Multimodal Machine Translation
on Multi30K
no code implementations • 1 Jan 2021 • Guanlin Li, Lemao Liu, Taro Watanabe, Conghui Zhu, Tiejun Zhao
Unsupervised Neural Machine Translation or UNMT has received great attention in recent years.
1 code implementation • 21 Dec 2020 • Wang Xu, Kehai Chen, Tiejun Zhao
In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years.
Ranked #35 on
Relation Extraction
on DocRED
no code implementations • SEMEVAL 2020 • Zhen Li, Yaojie Zhang, Bing Xu, Tiejun Zhao
Internet memes emotion recognition is focused by many researchers.
no code implementations • COLING 2020 • Zhenyu Zhao, Shuangzhi Wu, Muyun Yang, Kehai Chen, Tiejun Zhao
Neural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels.
1 code implementation • EMNLP 2020 • Changzhen Ji, Xin Zhou, Yating Zhang, Xiaozhong Liu, Changlong Sun, Conghui Zhu, Tiejun Zhao
In the past few years, audiences from different fields witness the achievements of sequence-to-sequence models (e. g., LSTM+attention, Pointer Generator Networks, and Transformer) to enhance dialogue content generation.
no code implementations • 22 Oct 2020 • Changzhen Ji, Xin Zhou, Conghui Zhu, Tiejun Zhao
The multi-role judicial debate composed of the plaintiff, defendant, and judge is an important part of the judicial trial.
no code implementations • COLING 2020 • Yingyao Wang, Junwei Bao, Guangyi Liu, Youzheng Wu, Xiaodong He, BoWen Zhou, Tiejun Zhao
This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations.
no code implementations • 15 Oct 2020 • Guanhua Zhang, Bing Bai, Jian Liang, Kun Bai, Conghui Zhu, Tiejun Zhao
Recent studies show that crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts.
no code implementations • WS 2020 • Xuancai Li, Chen Kehai, Tiejun Zhao, Muyun Yang
End-to-End speech translation usually leverages audio-to-text parallel data to train an available speech translation model which has shown impressive results on various speech translation tasks.
1 code implementation • ACL 2020 • Guanhua Zhang, Bing Bai, Junqi Zhang, Kun Bai, Conghui Zhu, Tiejun Zhao
In this paper, we formalize the unintended biases in text classification datasets as a kind of selection bias from the non-discrimination distribution to the discrimination distribution.
no code implementations • ACL 2020 • Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs.
no code implementations • NAACL 2021 • Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks.
no code implementations • 5 Apr 2020 • Conghui Zhu, Guanlin Li, Lemao Liu, Tiejun Zhao, Shuming Shi
Despite the great success of NMT, there still remains a severe challenge: it is hard to interpret the internal dynamics during its training process.
no code implementations • 5 Apr 2020 • Guanlin Li, Lemao Liu, Conghui Zhu, Tiejun Zhao, Shuming Shi
Generalization to unseen instances is our eternal pursuit for all data-driven models.
2 code implementations • CVPR 2020 • Mohan Zhou, Yalong Bai, Wei zhang, Tiejun Zhao, Tao Mei
Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category.
no code implementations • COLING 2020 • Haipeng Sun, Rui Wang, Kehai Chen, Xugang Lu, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
Unsupervised neural machine translation (UNMT) has recently attracted great interest in the machine translation community.
no code implementations • 28 Feb 2020 • Chaoqun Duan, Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Conghui Zhu, Tiejun Zhao
Existing neural machine translation (NMT) systems utilize sequence-to-sequence neural networks to generate target translation word by word, and then make the generated word at each time-step and the counterpart in the references as consistent as possible.
no code implementations • 7 Feb 2020 • Chaoqun Duan, Lei Cui, Shuming Ma, Furu Wei, Conghui Zhu, Tiejun Zhao
In this work, we aim to improve the relevance between live comments and videos by modeling the cross-modal interactions among different modalities.
no code implementations • IJCNLP 2019 • Guanlin Li, Lemao Liu, Guoping Huang, Conghui Zhu, Tiejun Zhao
Many Data Augmentation (DA) methods have been proposed for neural machine translation.
no code implementations • 10 Sep 2019 • Guanhua Zhang, Bing Bai, Junqi Zhang, Kun Bai, Conghui Zhu, Tiejun Zhao
This irregularity makes the evaluation results over-estimated and affects models' generalization ability.
no code implementations • 3 Sep 2019 • Xuefeng Bai, Yue Zhang, Hailong Cao, Tiejun Zhao
Unsupervised bilingual lexicon induction naturally exhibits duality, which results from symmetry in back-translation.
no code implementations • 26 Aug 2019 • Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao, Chenhui Chu
However, it has not been well-studied for unsupervised neural machine translation (UNMT), although UNMT has recently achieved remarkable results in several domain-specific language pairs.
no code implementations • ACL 2019 • Mingming Yang, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Min Zhang, Tiejun Zhao
The training objective of neural machine translation (NMT) is to minimize the loss between the words in the translated sentences and those in the references.
no code implementations • ACL 2019 • Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
In previous methods, UBWE is first trained using non-parallel monolingual corpora and then this pre-trained UBWE is used to initialize the word embedding in the encoder and decoder of UNMT.
no code implementations • NAACL 2019 • Guanlin Li, Lemao Liu, Xintong Li, Conghui Zhu, Tiejun Zhao, Shuming Shi
Multilayer architectures are currently the gold standard for large-scale neural machine translation.
no code implementations • NAACL 2019 • Chunpeng Ma, Akihiro Tamura, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
The explicit use of syntactic information has been proved useful for neural machine translation (NMT).
no code implementations • SEMEVAL 2019 • Yaojie Zhang, Bing Xu, Tiejun Zhao
Our macro-averaged F1-score in sub-task A is 0. 768, ranking 28/103.
2 code implementations • ACL 2019 • Guanhua Zhang, Bing Bai, Jian Liang, Kun Bai, Shiyu Chang, Mo Yu, Conghui Zhu, Tiejun Zhao
Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process.
no code implementations • ECCV 2018 • Yalong Bai, Jianlong Fu, Tiejun Zhao, Tao Mei
First, we model one of the pairwise interaction (e. g., image and question) by bilinear features, which is further encoded with the third dimension (e. g., answer) to be a triplet by bilinear tensor product.
1 code implementation • ACL 2018 • Qingyu Zhou, Nan Yang, Furu Wei, Shaohan Huang, Ming Zhou, Tiejun Zhao
In this paper, we present a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences.
Ranked #10 on
Extractive Text Summarization
on CNN / Daily Mail
no code implementations • ACL 2018 • Chunpeng Ma, Akihiro Tamura, Masao Utiyama, Tiejun Zhao, Eiichiro Sumita
Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors.
no code implementations • 29 May 2018 • Junwei Bao, Duyu Tang, Nan Duan, Zhao Yan, Yuanhua Lv, Ming Zhou, Tiejun Zhao
The model maps a row from a table to a continuous vector and then generates a natural language sentence by leveraging the semantics of a table.
no code implementations • 12 Nov 2017 • Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
In this paper, we extend local attention with syntax-distance constraint, to focus on syntactically related source words with the predicted target word, thus learning a more effective context vector for word prediction.
no code implementations • IJCNLP 2017 • Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
In Neural Machine Translation (NMT), each word is represented as a low-dimension, real-value vector for encoding its syntax and semantic information.
no code implementations • EMNLP 2017 • Kehai Chen, Rui Wang, Masao Utiyama, Lemao Liu, Akihiro Tamura, Eiichiro Sumita, Tiejun Zhao
Source dependency information has been successfully introduced into statistical machine translation.
no code implementations • 28 Aug 2017 • Yalong Bai, Kuiyuan Yang, Tao Mei, Wei-Ying Ma, Tiejun Zhao
Large scale image dataset and deep convolutional neural network (DCNN) are two primary driving forces for the rapid progress made in generic object recognition tasks in recent years.
1 code implementation • COLING 2016 • Junwei Bao, Nan Duan, Zhao Yan, Ming Zhou, Tiejun Zhao
WebQuestions and SimpleQuestions are two benchmark data-sets commonly used in recent knowledge-based question answering (KBQA) work.
no code implementations • COLING 2016 • Hailong Cao, Tiejun Zhao, Shu Zhang, Yao Meng
We introduce a distribution based model to learn bilingual word embeddings from monolingual data.
no code implementations • LREC 2016 • Huaxing Shi, Tiejun Zhao, Keh-Yih Su
This Treebank is a part of a semantic corpus building project, which aims to build a semantic bilingual corpus annotated with syntactic, semantic cases and word senses.
no code implementations • 1 Dec 2015 • Yiming Cui, Conghui Zhu, Xiaoning Zhu, Tiejun Zhao
Pivot language is employed as a way to solve the data sparseness problem in machine translation, especially when the data for a particular language pair does not exist.
no code implementations • 17 Dec 2013 • Yalong Bai, Kuiyuan Yang, Wei Yu, Wei-Ying Ma, Tiejun Zhao
Image retrieval refers to finding relevant images from an image database for a query, which is considered difficult for the gap between low-level representation of images and high-level representation of queries.