1 code implementation • COLING 2022 • Chenxu Yang, Zheng Lin, Jiangnan Li, Fandong Meng, Weiping Wang, Lanrui Wang, Jie zhou
The knowledge selector generally constructs a query based on the dialogue context and selects the most appropriate knowledge to help response generation.
1 code implementation • EMNLP 2021 • Yuan YAO, Jiaju Du, Yankai Lin, Peng Li, Zhiyuan Liu, Jie zhou, Maosong Sun
Existing relation extraction (RE) methods typically focus on extracting relational facts between entity pairs within single sentences or documents.
no code implementations • Findings (EMNLP) 2021 • Lei Shen, Jinchao Zhang, Jiao Ou, Xiaofang Zhao, Jie zhou
To address the above issues, we propose a dual-generative model, Dual-Emp, to simultaneously construct the emotional consensus and utilize some external unpaired data.
no code implementations • COLING 2022 • Jie zhou, Shenpo Dong, Hongkui Tu, Xiaodong Wang, Yong Dou
In this paper, we propose RSGT: Relational Structure Guided Temporal Relation Extraction to extract the relational structure features that can fit for both inter-sentence and intra-sentence relations.
Ranked #1 on
Temporal Relation Classification
on MATRES
Natural Language Understanding
Temporal Relation Classification
no code implementations • ECCV 2020 • Liangliang Ren, Yangyang Song, Jiwen Lu, Jie zhou
Unlike most existing works that define room layout on a 2D image, we model the layout in 3D as a configuration of the camera and the room.
1 code implementation • Findings (ACL) 2022 • Xin Lv, Yankai Lin, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie zhou
In recent years, pre-trained language models (PLMs) have been shown to capture factual knowledge from massive texts, which encourages the proposal of PLM-based knowledge graph completion (KGC) models.
no code implementations • ACL 2022 • Hui Su, Weiwei Shi, Xiaoyu Shen, Zhou Xiao, Tuo ji, Jiarui Fang, Jie zhou
Large-scale pretrained language models have achieved SOTA results on NLP tasks.
no code implementations • EMNLP 2020 • Hui Su, Xiaoyu Shen, Zhou Xiao, Zheng Zhang, Ernie Chang, Cheng Zhang, Cheng Niu, Jie zhou
In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots.
no code implementations • ECCV 2020 • Guangyi Chen, Yongming Rao, Jiwen Lu, Jie zhou
Specifically, we disentangle the video representation into the temporal coherence and motion parts and randomly change the scale of the temporal motion features as the adversarial noise.
no code implementations • ECCV 2020 • Guangyi Chen, Yuhao Lu, Jiwen Lu, Jie Zhou
Experimental results demonstrate that our DCML method explores credible and valuable training data and improves the performance of unsupervised domain adaptation.
no code implementations • SemEval (NAACL) 2022 • Qi Zhang, Jie zhou, Qin Chen, Qingchun Bai, Jun Xiao, Liang He
The task aims to extract the structured sentiment information (e. g., holder, target, expression and sentiment polarity) in a text.
1 code implementation • ACL 2022 • Yikang Shen, Shawn Tan, Alessandro Sordoni, Peng Li, Jie zhou, Aaron Courville
We introduce a new model, the Unsupervised Dependency Graph Network (UDGN), that can induce dependency structures from raw corpora and the masked language modeling task.
no code implementations • ACL 2022 • Kunyuan Pang, Haoyu Zhang, Jie zhou, Ting Wang
In this work, we propose a clustering-based loss correction framework named Feature Cluster Loss Correction (FCLC), to address these two problems.
no code implementations • ECCV 2020 • Ziwei Wang, Quan Zheng, Jiwen Lu, Jie zhou
n this paper, we propose a Deep Hashing method with Active Pairwise Supervision(DH-APS).
1 code implementation • ACL 2022 • Xu Han, Guoyang Zeng, Weilin Zhao, Zhiyuan Liu, Zhengyan Zhang, Jie zhou, Jun Zhang, Jia Chao, Maosong Sun
In recent years, large-scale pre-trained language models (PLMs) containing billions of parameters have achieved promising results on various NLP tasks.
no code implementations • ECCV 2020 • Yu Zheng, Danyang Zhang, Sinan Xie, Jiwen Lu, Jie zhou
In this paper, we propose a Rotation-robust Intersection over Union ($ extit{RIoU}$) for 3D object detection, which aims to jointly learn the overlap of rotated bounding boxes.
no code implementations • ECCV 2020 • Wenzhao Zheng, Jiwen Lu, Jie zhou
We employ a metric model and a layout encoder to map the RGB images and the ground-truth layouts to the embedding space, respectively, and a layout decoder to map the embeddings to the corresponding layouts, where the whole framework is trained in an end-to-end manner.
no code implementations • CCL 2021 • Shan Wang, Jie zhou
“欺骗是一种常见的社会现象, 但对欺骗类动词的研究十分有限。本文筛选“欺骗”类动词的单句并对其进行大规模的句法依存和语义依存分析。研究显示,“欺骗”类动词在句中作为从属词时, 可作为不同的句法成分和语义角色, 同时此类动词在句法功能上表现出高度的相似性。作为支配词的“欺骗”类动词, 承担不同句法功能时, 表现出不同的句法共现模式。语义上, 本文详细描述、解释了该类动词在语义密度、主客体角色、情境角色和事件关系等维度的语义依存特点。“欺骗”类动词的句法语义虽具有多样性, 但主要的句型为主谓宾句式, 而该句式中最常用的语义搭配模式是施事对涉事进行欺骗行为, 并对涉事产生影响。本研究结合依存语法和框架语义学, 融合定量统计和定性分析探究欺骗类动词的句法语义, 深化了对欺骗行为言语线索以及言说动词的研究。”
no code implementations • EMNLP 2020 • Xiuyi Chen, Fandong Meng, Peng Li, Feilong Chen, Shuang Xu, Bo Xu, Jie zhou
Here, we deal with these issues on two aspects: (1) We enhance the prior selection module with the necessary posterior information obtained from the specially designed Posterior Information Prediction Module (PIPM); (2) We propose a Knowledge Distillation Based Training Strategy (KDBTS) to train the decoder with the knowledge selected from the prior distribution, removing the exposure bias of knowledge selection.
no code implementations • Findings (ACL) 2022 • Le Tian, Houjin Yu, Zhou Xiao, Hui Su, Jie zhou
Prompt-based paradigm has shown its competitive performance in many NLP tasks.
no code implementations • 30 Nov 2023 • Yongjie Duan, Zhiyu Pan, Jianjiang Feng, Jie zhou
The matching scores produced by LDRF also exhibit intuitive statistical characteristics, which led us to propose a matching score normalization technique to mitigate the uncertainty in the cases of very small overlapping area.
1 code implementation • 21 Nov 2023 • Yuanhui Huang, Wenzhao Zheng, Borui Zhang, Jie zhou, Jiwen Lu
Our SelfOcc outperforms the previous best method SceneRF by 58. 7% using a single frame as input on SemanticKITTI and is the first self-supervised work that produces reasonable 3D occupancy for surround cameras on nuScenes.
1 code implementation • 20 Nov 2023 • Xin Zhang, Yingze Song, Tingting Song, Degang Yang, Yichen Ye, Jie zhou, Liming Zhang
In response to the above questions, the Alterable Kernel Convolution (AKConv) is explored in this work, which gives the convolution kernel an arbitrary number of parameters and arbitrary sampled shapes to provide richer options for the trade-off between network overhead and performance.
2 code implementations • 20 Nov 2023 • Bohao Fan, Wenzhao Zheng, Jianjiang Feng, Jie zhou
In recent years, point cloud perception tasks have been garnering increasing attention.
no code implementations • 15 Nov 2023 • Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie zhou, Juanzi Li
Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships.
no code implementations • 15 Nov 2023 • Wenkai Yang, Yankai Lin, Jie zhou, JiRong Wen
That is, humans can grasp the new tasks or knowledge quickly and generalize well given only a detailed rule and a few optional examples.
no code implementations • 14 Nov 2023 • Yi Liu, Lianzhe Huang, Shicheng Li, Sishuo Chen, Hao Zhou, Fandong Meng, Jie zhou, Xu sun
Therefore, to evaluate the ability of LLMs to discern the reliability of external knowledge, we create a benchmark from existing knowledge bases.
1 code implementation • 14 Nov 2023 • Kunting Li, Yong Hu, Shaolei Wang, Hanhan Ma, Liang He, Fandong Meng, Jie zhou
However, in the Chinese Spelling Correction (CSC) task, we observe a discrepancy: while ChatGPT performs well under human evaluation, it scores poorly according to traditional metrics.
no code implementations • 8 Nov 2023 • Zhen Yang, Yingxue Zhang, Fandong Meng, Jie zhou
Specifically, for the input from any modality, TEAL first discretizes it into a token sequence with the off-the-shelf tokenizer and embeds the token sequence into a joint embedding space with a learnable embedding matrix.
no code implementations • 6 Nov 2023 • Jiali Zeng, Fandong Meng, Yongjing Yin, Jie zhou
Contemporary translation engines built upon the encoder-decoder framework have reached a high level of development, while the emergence of Large Language Models (LLMs) has disrupted their position by offering the potential for achieving superior translation quality.
no code implementations • 3 Nov 2023 • Wenqi Sun, Ruobing Xie, Shuqing Bian, Wayne Xin Zhao, Jie zhou
There is a rapidly-growing research interest in modeling user preferences via pre-training multi-domain interactions for recommender systems.
no code implementations • 3 Nov 2023 • Shicheng Xu, Liang Pang, Jiangnan Li, Mo Yu, Fandong Meng, HuaWei Shen, Xueqi Cheng, Jie zhou
Readers usually only give an abstract and vague description as the query based on their own understanding, summaries, or speculations of the plot, which requires the retrieval model to have a strong ability to estimate the abstract semantic associations between the query and candidate plots.
1 code implementation • 2 Nov 2023 • Borui Zhang, Baotong Tian, Wenzhao Zheng, Jie zhou, Jiwen Lu
Shapley values have emerged as a widely accepted and trustworthy tool, grounded in theoretical axioms, for addressing challenges posed by black-box models like deep neural networks.
1 code implementation • NeurIPS 2023 • Yinan Liang, Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie zhou, Jiwen Lu
Due to the high price and heavy energy consumption of GPUs, deploying deep models on IoT devices such as microcontrollers makes significant contributions for ecological AI.
1 code implementation • 24 Oct 2023 • Chaojun Xiao, Yuqi Luo, Wenbin Zhang, Pengle Zhang, Xu Han, Yankai Lin, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie zhou
Pre-trained language models (PLMs) have achieved remarkable results on NLP tasks but at the expense of huge parameter sizes and the consequent computational costs.
no code implementations • 20 Oct 2023 • Zekai Qu, Ruobing Xie, Chaojun Xiao, Yuan YAO, Zhiyuan Liu, Fengzong Lian, Zhanhui Kang, Jie zhou
With the thriving of pre-trained language model (PLM) widely verified in various of NLP tasks, pioneer efforts attempt to explore the possible cooperation of the general textual information in PLM with the personalized behavioral information in user historical behavior sequences to enhance sequential recommendation (SR).
no code implementations • 19 Oct 2023 • Weize Chen, Xiaoyue Xu, Xu Han, Yankai Lin, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie zhou
Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resource-constrained environments, enabling substantial reductions in model storage and memory costs without significant performance compromise.
no code implementations • 18 Oct 2023 • Jie zhou, Qian Yu
The model has three innovations: 1) It adopts the idea of cross network and uses RNN network to cross-process the features, thereby effectively improves the expressive ability of the model; 2) It innovatively proposes the structure of partial parameter sharing; 3) It can effectively capture the potential correlation between different tasks to optimize the efficiency and methods for learning different tasks.
1 code implementation • 14 Oct 2023 • Junjie Ye, Jie zhou, Junfeng Tian, Rui Wang, Qi Zhang, Tao Gui, Xuanjing Huang
Recently, Target-oriented Multimodal Sentiment Classification (TMSC) has gained significant attention among scholars.
1 code implementation • 9 Oct 2023 • Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Fang Guo, Qinglin Qi, Jie zhou, Yue Zhang
During the selection of unlabeled data, we combine the predictive uncertainty of the encoder and the explanation score of the decoder to acquire informative data for annotation.
no code implementations • 9 Oct 2023 • Ruizhi Wang, Xiangtao Wang, Jie zhou, Thomas Lukasiewicz, Zhenghua Xu
In addition, word-level optimization based on numbers ignores the semantics of reports and medical images, and the generated reports often cannot achieve good performance.
1 code implementation • 8 Oct 2023 • Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Jie zhou, Yue Zhang
However, we observe that merely concatenating sentences in a contextual window does not fully utilize contextual information and can sometimes lead to excessive attention on less informative sentences.
no code implementations • ICCV 2023 • Zhiheng Li, Wenjia Geng, Muheng Li, Lei Chen, Yansong Tang, Jiwen Lu, Jie zhou
By this means, our model explores all sorts of reliable sub-relations within an action sequence in the condensed action space.
1 code implementation • ICCV 2023 • Junlong Li, Bingyao Yu, Yongming Rao, Jie zhou, Jiwen Lu
The core of our method consists of a global instance assignment strategy and a spatio-temporal enhancement module, which improve the temporal consistency of the features from two aspects.
2 code implementations • 11 Sep 2023 • Chengkun Wang, Wenzhao Zheng, Zheng Zhu, Jie zhou, Jiwen Lu
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images.
no code implementations • 9 Sep 2023 • Yifan Dong, Suhang Wu, Fandong Meng, Jie zhou, Xiaoli Wang, Jianxin Lin, Jinsong Su
2) the input text and image are often not perfectly matched, and thus the image may introduce noise into the model.
no code implementations • 8 Sep 2023 • Xiangtao Wang, Ruizhi Wang, Jie zhou, Thomas Lukasiewicz, Zhenghua Xu
The proposed strategies effectively address limitations in applying masked modeling to medical images, tailored to capturing fine lesion details vital for segmentation tasks.
no code implementations • 7 Sep 2023 • Chujie Zheng, Hao Zhou, Fandong Meng, Jie zhou, Minlie Huang
Through extensive empirical analyses with 20 LLMs on three benchmarks, we pinpoint that this behavioral bias primarily stems from LLMs' token bias, where the model a priori assigns more probabilistic mass to specific option ID tokens (e. g., A/B/C/D) when predicting answers from the option IDs.
no code implementations • 2 Sep 2023 • Sanyi Zhang, Xiaochun Cao, Rui Wang, Guo-Jun Qi, Jie zhou
The experimental results show that the proposed method demonstrates good universality which can improve the robustness of the human parsing models and even the semantic segmentation models when facing various image common corruptions.
1 code implementation • 31 Aug 2023 • Sicheng Zuo, Wenzhao Zheng, Yuanhui Huang, Jie zhou, Jiwen Lu
To address this, we propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively and a PointOcc model to process them efficiently.
1 code implementation • 24 Aug 2023 • Yijie Chen, Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jie zhou
The experimental results demonstrate significant improvements in translation performance with SWIE based on BLOOMZ-3b, particularly in zero-shot and long text translations due to reduced instruction forgetting risk.
1 code implementation • 23 Aug 2023 • Yijin Liu, Xianfeng Zeng, Fandong Meng, Jie zhou
Large language models (LLMs) are capable of performing conditional sequence generation tasks, such as translation or summarization, through instruction fine-tuning.
1 code implementation • 21 Aug 2023 • Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie zhou
Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks.
1 code implementation • 17 Aug 2023 • Yun Luo, Zhen Yang, Fandong Meng, Yafu Li, Jie zhou, Yue Zhang
Moreover, we find that ALPACA can maintain more knowledge and capacity compared with LLAMA during the continual fine-tuning, which implies that general instruction tuning can help mitigate the forgetting phenomenon of LLMs in the further fine-tuning process.
1 code implementation • 6 Aug 2023 • Xianfeng Zeng, Yijin Liu, Fandong Meng, Jie zhou
To address this issue, we propose to utilize \textit{multiple references} to enhance the consistency between these metrics and human evaluations.
1 code implementation • 5 Aug 2023 • Yuhao Dan, Zhikai Lei, Yiyang Gu, Yong Li, Jianghao Yin, Jiaju Lin, Linhao Ye, Zhiyan Tie, Yougen Zhou, Yilei Wang, Aimin Zhou, Ze Zhou, Qin Chen, Jie zhou, Liang He, Xipeng Qiu
Currently, EduChat is available online as an open-source project, with its code, data, and model parameters available on platforms (e. g., GitHub https://github. com/icalk-nlp/EduChat, Hugging Face https://huggingface. co/ecnu-icalk ).
1 code implementation • 1 Aug 2023 • Bohao Fan, Siqi Wang, Wenxuan Guo, Wenzhao Zheng, Jianjiang Feng, Jie zhou
In this article, we propose Human-M3, an outdoor multi-modal multi-view multi-person human pose database which includes not only multi-view RGB videos of outdoor scenes but also corresponding pointclouds.
1 code implementation • 31 Jul 2023 • Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Lauren Hong, Runchu Tian, Ruobing Xie, Jie zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, Maosong Sun
Based on ToolBench, we fine-tune LLaMA to obtain an LLM ToolLLaMA, and equip it with a neural API retriever to recommend appropriate APIs for each instruction.
no code implementations • 29 Jul 2023 • Lean Wang, Wenkai Yang, Deli Chen, Hao Zhou, Yankai Lin, Fandong Meng, Jie zhou, Xu sun
As large language models (LLMs) generate texts with increasing fluency and realism, there is a growing need to identify the source of texts to prevent the abuse of LLMs.
1 code implementation • ICCV 2023 • Ziyi Wang, Xumin Yu, Yongming Rao, Jie zhou, Jiwen Lu
In this paper, we propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model.
Ranked #6 on
3D Part Segmentation
on ShapeNet-Part
1 code implementation • 10 Jul 2023 • Jiali Zeng, Fandong Meng, Yongjing Yin, Jie zhou
Open-sourced large language models (LLMs) have demonstrated remarkable efficacy in various tasks with instruction tuning.
no code implementations • 13 Jun 2023 • Jiali Zeng, Yufan Jiang, Yongjing Yin, Yi Jing, Fandong Meng, Binghuai Lin, Yunbo Cao, Jie zhou
Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from source languages or when pre-training data is limited in size.
no code implementations • 30 May 2023 • Changyuan Wang, Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie zhou, Jiwen Lu
On the contrary, we design group-wise quantization functions for activation discretization in different timesteps and sample the optimal timestep for informative calibration image generation, so that our quantized diffusion model can reduce the discretization errors with negligible computational overhead.
1 code implementation • 28 May 2023 • Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Chaojun Xiao, Xiaozhi Wang, Xu Han, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Jie zhou
In analogy to human brains, we consider two main characteristics of modularity: (1) functional specialization of neurons: we evaluate whether each neuron is mainly specialized in a certain function, and find that the answer is yes.
1 code implementation • 28 May 2023 • Weize Chen, Xu Han, Yankai Lin, Zhiyuan Liu, Maosong Sun, Jie zhou
Since it is non-trivial to directly model the intermediate states and design a running cost function, we propose to use latent stochastic bridges to regularize the intermediate states and use the regularization as the running cost of PETs.
1 code implementation • 28 May 2023 • Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Huadong Wang, Deming Ye, Chaojun Xiao, Xu Han, Zhiyuan Liu, Peng Li, Maosong Sun, Jie zhou
Experimental results on three knowledge-driven NLP tasks show that existing injection methods are not suitable for the new paradigm, while map-tuning effectively improves the performance of downstream models.
no code implementations • 23 May 2023 • Lean Wang, Lei LI, Damai Dai, Deli Chen, Hao Zhou, Fandong Meng, Jie zhou, Xu sun
In-context learning (ICL) emerges as a promising capability of large language models (LLMs) by providing them with demonstration examples to perform diverse tasks.
1 code implementation • 22 May 2023 • Xiao Wang, Weikang Zhou, Qi Zhang, Jie zhou, Songyang Gao, Junzhe Wang, Menghan Zhang, Xiang Gao, Yunwen Chen, Tao Gui
Pretrained language models have achieved remarkable success in various natural language processing tasks.
1 code implementation • 22 May 2023 • Yunlong Liang, Fandong Meng, Jiaan Wang, Jinan Xu, Yufeng Chen, Jie zhou
Further, we propose a dual knowledge distillation and target-oriented vision modeling framework for the M$^3$S task.
1 code implementation • 21 May 2023 • Limao Xiong, Jie zhou, Qunxi Zhu, Xiao Wang, Yuanbin Wu, Qi Zhang, Tao Gui, Xuanjing Huang, Jin Ma, Ying Shan
Particularly, we propose a Confidence-based Partial Label Learning (CPLL) method to integrate the prior confidence (given by annotators) and posterior confidences (learned by models) for crowd-annotated NER.
no code implementations • 20 May 2023 • Mingjie Cai, Zhishan Wu, Qingguo Li, Feng Xu, Jie zhou
Further, three novel granule fusion strategies are utilized to combine granules into stable cluster structures, helping to detect clusters with arbitrary shapes.
no code implementations • 20 May 2023 • Yun Luo, Xiaotian Lin, Zhen Yang, Fandong Meng, Jie zhou, Yue Zhang
It is seldom considered to adapt the decision boundary for new representations and in this paper we propose a Supervised Contrastive learning framework with adaptive classification criterion for Continual Learning (SCCL), In our method, a contrastive loss is used to directly learn representations for different tasks and a limited number of data samples are saved as the classification criterion.
2 code implementations • NeurIPS 2023 • Wenhai Wang, Zhe Chen, Xiaokang Chen, Jiannan Wu, Xizhou Zhu, Gang Zeng, Ping Luo, Tong Lu, Jie zhou, Yu Qiao, Jifeng Dai
We hope this model can set a new baseline for generalist vision and language models.
1 code implementation • 17 May 2023 • Mo Yu, Jiangnan Li, Shunyu Yao, Wenjie Pang, Xiaochen Zhou, Zhou Xiao, Fandong Meng, Jie zhou
As readers engage with a story, their understanding of a character evolves based on new events and information; and multiple fine-grained aspects of personalities can be perceived.
no code implementations • 16 May 2023 • Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie zhou
In this paper, we aim to unify MLS and CLS into a more general setting, i. e., many-to-many summarization (M2MS), where a single model could process documents in any language and generate their summaries also in any language.
1 code implementation • 15 May 2023 • Yujia Qin, Cheng Qian, Xu Han, Yankai Lin, Huadong Wang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie zhou
In pilot studies, we find that after continual pre-training, the upgraded PLM remains compatible with the outdated adapted weights to some extent.
no code implementations • 13 May 2023 • Chulun Zhou, Yunlong Liang, Fandong Meng, Jinan Xu, Jinsong Su, Jie zhou
In this paper, we propose Regularized Contrastive Cross-lingual Cross-modal (RC^3) pre-training, which further exploits more abundant weakly-aligned multilingual image-text pairs.
no code implementations • 11 May 2023 • Mingliang Zhang, Zhen Cao, Juntao Liu, LiQiang Niu, Fandong Meng, Jie zhou
Our approach effectively demonstrates the benefits of combining query-based and anchor-free models for achieving robust layout segmentation in corporate documents.
1 code implementation • 11 May 2023 • Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie zhou
We recruit annotators to search for relevant information using our interface and then answer questions.
no code implementations • 10 May 2023 • Yun Luo, Zhen Yang, Xuefeng Bai, Fandong Meng, Jie zhou, Yue Zhang
Intuitively, the representation forgetting can influence the general knowledge stored in pre-trained language models (LMs), but the concrete effect is still unclear.
no code implementations • 8 May 2023 • Zhiyuan Zhang, Deli Chen, Hao Zhou, Fandong Meng, Jie zhou, Xu sun
To settle this issue, we propose the Fine-purifying approach, which utilizes the diffusion theory to study the dynamic process of fine-tuning for finding potentially poisonous dimensions.
1 code implementation • 6 May 2023 • Yiqing Wu, Ruobing Xie, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Jie zhou, Yongjun Xu, Qing He
Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained recommendation model for different downstream recommendation tasks.
1 code implementation • 5 May 2023 • Xiuwei Xu, Zhihao Sun, Ziwei Wang, Hongmin Liu, Jie zhou, Jiwen Lu
We organize two benchmarks on ScanNet and TO-SCENE dataset to evaluate the ability of fine-grained 3D object detection, where our DSPDet3D improves the detection performance of small objects to a new level while achieving leading inference speed compared with existing 3D object detection methods.
no code implementations • 4 May 2023 • Yijin Liu, Xianfeng Zeng, Fandong Meng, Jie zhou
Recently, DeepNorm scales Transformers into extremely deep (i. e., 1000 layers) and reveals the promising potential of deep scaling.
no code implementations • 4 May 2023 • Yunlong Liang, Fandong Meng, Jinan Xu, Jiaan Wang, Yufeng Chen, Jie zhou
Specifically, we propose a ``versatile'' model, i. e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks, and can translate well in multiple settings simultaneously, and theoretically it can be as many as possible.
1 code implementation • 13 Apr 2023 • Ziwei Wang, Jiwen Lu, Han Xiao, Shengyu Liu, Jie zhou
On the contrary, we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor, where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation.
no code implementations • 11 Apr 2023 • Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin, Jie zhou
To address this issue, we present a novel framework, termed triple sequence learning for cross-domain recommendation (Tri-CDR), which jointly models the source, target, and mixed behavior sequences to highlight the global and target preference and precisely model the triple correlation in CDR.
no code implementations • CVPR 2023 • Xiuwei Xu, Ziwei Wang, Jie zhou, Jiwen Lu
In this paper, we propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis.
1 code implementation • 23 Mar 2023 • Xiaoke Huang, Yiji Cheng, Yansong Tang, Xiu Li, Jie zhou, Jiwen Lu
Moreover, only minutes of optimization is enough for plausible reconstruction results.
no code implementations • 18 Mar 2023 • Junjie Ye, Xuanting Chen, Nuo Xu, Can Zu, Zekai Shao, Shichun Liu, Yuhan Cui, Zeyang Zhou, Chao Gong, Yang shen, Jie zhou, Siming Chen, Tao Gui, Qi Zhang, Xuanjing Huang
GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities.
2 code implementations • ICCV 2023 • Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Jie zhou, Jiwen Lu
Towards a more comprehensive perception of a 3D scene, in this paper, we propose a SurroundOcc method to predict the 3D occupancy with multi-camera images.
no code implementations • 14 Mar 2023 • Jun Wan, Jun Liu, Jie zhou, Zhihui Lai, Linlin Shen, Hang Sun, Ping Xiong, Wenwen Min
Most facial landmark detection methods predict landmarks by mapping the input facial appearance features to landmark heatmaps and have achieved promising results.
1 code implementation • 10 Mar 2023 • Jie zhou, Xianshuai Cao, Wenhao Li, Lin Bo, Kun Zhang, Chuan Luo, Qian Yu
Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications, wherein an effective and practical approach is to carry out multi-scenario transfer learning on the basis of the Mixture-of-Expert (MoE) architecture.
1 code implementation • 7 Mar 2023 • Jiaan Wang, Yunlong Liang, Fandong Meng, Zengkui Sun, Haoxiang Shi, Zhixu Li, Jinan Xu, Jianfeng Qu, Jie zhou
In detail, we regard ChatGPT as a human evaluator and give task-specific (e. g., summarization) and aspect-specific (e. g., relevance) instruction to prompt ChatGPT to evaluate the generated results of NLG models.
2 code implementations • ICCV 2023 • Wenliang Zhao, Yongming Rao, Zuyan Liu, Benlin Liu, Jie zhou, Jiwen Lu
In this paper, we propose VPD (Visual Perception with a pre-trained Diffusion model), a new framework that exploits the semantic information of a pre-trained text-to-image diffusion model in visual perception tasks.
Ranked #4 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
no code implementations • 1 Mar 2023 • Xuanting Chen, Junjie Ye, Can Zu, Nuo Xu, Rui Zheng, Minlong Peng, Jie zhou, Tao Gui, Qi Zhang, Xuanjing Huang
The GPT-3. 5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks, showcasing their strong understanding and reasoning capabilities.
Natural Language Inference
Natural Language Understanding
+1
no code implementations • 28 Feb 2023 • Jiaan Wang, Yunlong Liang, Fandong Meng, Beiqi Zou, Zhixu Li, Jianfeng Qu, Jie zhou
Given a document in a source language, cross-lingual summarization (CLS) aims to generate a summary in a different target language.
no code implementations • 21 Feb 2023 • Meng Zhang, Wenxuan Guo, Bohao Fan, Yifan Chen, Jianjiang Feng, Jie zhou
The experimental results show that multi-view point clouds greatly improve 3D object detection and tracking accuracy regardless of complex and various outdoor environments.
no code implementations • 21 Feb 2023 • Zhenxiao Cheng, Jie zhou, Wen Wu, Qin Chen, Liang He
Gradient-based explanation methods play an important role in the field of interpreting complex deep neural networks for NLP models.
2 code implementations • CVPR 2023 • Yuanhui Huang, Wenzhao Zheng, Yunpeng Zhang, Jie zhou, Jiwen Lu
To lift image features to the 3D TPV space, we further propose a transformer-based TPV encoder (TPVFormer) to obtain the TPV features effectively.
Ranked #5 on
3D Semantic Scene Completion
on KITTI-360
1 code implementation • 10 Feb 2023 • Jie zhou, Qian Yu, Chuan Luo, Jing Zhang
In recent years, thanks to the rapid development of deep learning (DL), DL-based multi-task learning (MTL) has made significant progress, and it has been successfully applied to recommendation systems (RS).
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 • 25 Jan 2023 • Wenkai Yang, Deli Chen, Hao Zhou, Fandong Meng, Jie zhou, Xu sun
Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively in a data privacy-preserving manner.
no code implementations • 25 Jan 2023 • Wenkai Yang, Yankai Lin, Guangxiang Zhao, Peng Li, Jie zhou, Xu sun
Federated Learning has become a widely-used framework which allows learning a global model on decentralized local datasets under the condition of protecting local data privacy.
1 code implementation • 24 Jan 2023 • Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie zhou, Wenge Rong, Zhang Xiong
Our method outperforms previous fine-tuning and HyperNetwork-based methods and achieves state-of-the-art performance for Sequential Model Editing (SME).
1 code implementation • 11 Jan 2023 • Xumin Yu, Yongming Rao, Ziyi Wang, Jiwen Lu, Jie zhou
In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr, which adopts a Transformer encoder-decoder architecture for point cloud completion.
1 code implementation • CVPR 2023 • Shuai Shen, Wenliang Zhao, Zibin Meng, Wanhua Li, Zheng Zhu, Jie zhou, Jiwen Lu
In this way, the proposed DiffTalk is capable of producing high-quality talking head videos in synchronization with the source audio, and more importantly, it can be naturally generalized across different identities without any further fine-tuning.
no code implementations • ICCV 2023 • Shuai Shen, Wanhua Li, Xiaobing Wang, Dafeng Zhang, Zhezhu Jin, Jie zhou, Jiwen Lu
Furthermore, we develop a neighbor-aware proxy generator that fuses the features describing various attributes into a proxy feature to build a bridge among different sub-clusters and reduce the intra-class variance.
1 code implementation • CVPR 2023 • Shiyi Zhang, Wenxun Dai, Sujia Wang, Xiangwei Shen, Jiwen Lu, Jie zhou, Yansong Tang
Action quality assessment (AQA) has become an emerging topic since it can be extensively applied in numerous scenarios.
1 code implementation • CVPR 2023 • Chengkun Wang, Wenzhao Zheng, Junlong Li, Jie zhou, Jiwen Lu
Learning a generalizable and comprehensive similarity metric to depict the semantic discrepancies between images is the foundation of many computer vision tasks.
1 code implementation • CVPR 2023 • Wenliang Zhao, Yongming Rao, Weikang Shi, Zuyan Liu, Jie zhou, Jiwen Lu
Unlike previous work that relies on carefully designed network architectures and loss functions to fuse the information from the source and target faces, we reformulate the face swapping as a conditional inpainting task, performed by a powerful diffusion model guided by the desired face attributes (e. g., identity and landmarks).
no code implementations • 22 Dec 2022 • Helda Pahlavani, Kostas Tsifoutis-Kazolis, Prerak Mody, Jie zhou, Mohammad J. Mirzaali, Amir A. Zadpoor
Practical applications of mechanical metamaterials often involve solving inverse problems where the objective is to find the (multiple) microarchitectures that give rise to a given set of properties.
1 code implementation • 18 Dec 2022 • Borui Zhang, Wenzhao Zheng, Jie zhou, Jiwen Lu
Deep learning has revolutionized human society, yet the black-box nature of deep neural networks hinders further application to reliability-demanded industries.
1 code implementation • 15 Dec 2022 • Yunlong Liang, Fandong Meng, Jinan Xu, Jiaan Wang, Yufeng Chen, Jie zhou
However, less attention has been paid to the visual features from the perspective of the summary, which may limit the model performance, especially in the low- and zero-resource scenarios.
no code implementations • 14 Dec 2022 • Jiaan Wang, Fandong Meng, Yunlong Liang, Tingyi Zhang, Jiarong Xu, Zhixu Li, Jie zhou
In detail, we find that (1) the translationese in documents or summaries of test sets might lead to the discrepancy between human judgment and automatic evaluation; (2) the translationese in training sets would harm model performance in real-world applications; (3) though machine-translated documents involve translationese, they are very useful for building CLS systems on low-resource languages under specific training strategies.
no code implementations • CVPR 2023 • Yansong Tang, Jinpeng Liu, Aoyang Liu, Bin Yang, Wenxun Dai, Yongming Rao, Jiwen Lu, Jie zhou, Xiu Li
With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision.
no code implementations • 8 Dec 2022 • Jianhao Yan, Jin Xu, Fandong Meng, Jie zhou, Yue Zhang
In this work, we show that the issue arises from the un-consistency of label smoothing on the token-level and sequence-level distributions.
1 code implementation • CVPR 2023 • Muheng Li, Yueqi Duan, Jie zhou, Jiwen Lu
With the rising industrial attention to 3D virtual modeling technology, generating novel 3D content based on specified conditions (e. g. text) has become a hot issue.
no code implementations • 30 Nov 2022 • Chenze Shao, Jinchao Zhang, Jie zhou, Yang Feng
In response to this problem, we introduce a rephraser to provide a better training target for NAT by rephrasing the reference sentence according to the NAT output.
no code implementations • 30 Nov 2022 • Zhen Yang, Fandong Meng, Yingxue Zhang, Ernan Li, Jie zhou
We report the result of the first edition of the WMT shared task on Translation Suggestion (TS).
1 code implementation • 29 Nov 2022 • Jiaxin Wen, Yeshuang Zhu, Jinchao Zhang, Jie zhou, Minlie Huang
Recent studies have shown the impressive efficacy of counterfactually augmented data (CAD) for reducing NLU models' reliance on spurious features and improving their generalizability.
no code implementations • 28 Nov 2022 • Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie zhou
Our systems achieve 0. 810 and 0. 946 COMET scores.
no code implementations • 28 Nov 2022 • Ernan Li, Fandong Meng, Jie zhou
This paper introduces WeChat's participation in WMT 2022 shared biomedical translation task on Chinese to English.
no code implementations • 26 Nov 2022 • Jie zhou, Yefei Wang, Yiyang Yuan, Qing Huang, Jinshan Zeng
Numerical results show that the mode collapse issue suffered by the known CycleGAN can be effectively alleviated by equipping with the proposed SGCE module, and the CycleGAN equipped with SGCE outperforms the state-of-the-art models in terms of four important evaluation metrics and visualization quality.
no code implementations • 19 Nov 2022 • Zhixiang Zhang, Biao Jie, Zhengdong Wang, Jie zhou, Yang Yang
Recent studies have applied deep learning methods such as convolutional recurrent neural networks (CRNs) and Transformers to brain disease classification based on dynamic functional connectivity networks (dFCNs), such as Alzheimer's disease (AD), achieving better performance than traditional machine learning methods.
2 code implementations • CVPR 2023 • Chenyu Yang, Yuntao Chen, Hao Tian, Chenxin Tao, Xizhou Zhu, Zhaoxiang Zhang, Gao Huang, Hongyang Li, Yu Qiao, Lewei Lu, Jie zhou, Jifeng Dai
The proposed method is verified with a wide spectrum of traditional and modern image backbones and achieves new SoTA results on the large-scale nuScenes dataset.
Ranked #4 on
3D Object Detection
on Rope3D
1 code implementation • CVPR 2023 • Weijie Su, Xizhou Zhu, Chenxin Tao, Lewei Lu, Bin Li, Gao Huang, Yu Qiao, Xiaogang Wang, Jie zhou, Jifeng Dai
It has been proved that combining multiple pre-training strategies and data from various modalities/sources can greatly boost the training of large-scale models.
Ranked #2 on
Object Detection
on LVIS v1.0 minival
(using extra training data)
no code implementations • 17 Nov 2022 • Sichao Huang, Ziwei Wang, Jie zhou, Jiwen Lu
We compare our approach with existing robotic packing methods for irregular objects in a physics simulator.
1 code implementation • 17 Nov 2022 • Haojun Jiang, Jianke Zhang, Rui Huang, Chunjiang Ge, Zanlin Ni, Jiwen Lu, Jie zhou, Shiji Song, Gao Huang
However, as pre-trained models are scaling up, fully fine-tuning them on text-video retrieval datasets has a high risk of overfitting.
1 code implementation • 16 Nov 2022 • Yong Hu, Fandong Meng, Jie zhou
In fact, most of Chinese input is based on pinyin input method, so the study of spelling errors in this process is more practical and valuable.
1 code implementation • 15 Nov 2022 • Chengkun Wang, Wenzhao Zheng, Xian Sun, Jiwen Lu, Jie zhou
We propose to learn a global probabilistic distribution for each pixel in the patch and a probabilistic metric to model the distance between distributions.
1 code implementation • 14 Nov 2022 • Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie zhou
It contains 103, 193 event coreference chains, 1, 216, 217 temporal relations, 57, 992 causal relations, and 15, 841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude.
1 code implementation • 10 Nov 2022 • Xiaowei Hu, Min Shi, Weiyun Wang, Sitong Wu, Linjie Xing, Wenhai Wang, Xizhou Zhu, Lewei Lu, Jie zhou, Xiaogang Wang, Yu Qiao, Jifeng Dai
Our experiments on various tasks and an analysis of inductive bias show a significant performance boost due to advanced network-level and block-level designs, but performance differences persist among different STMs.
1 code implementation • 9 Nov 2022 • Mo Yu, Yisi Sang, Kangsheng Pu, Zekai Wei, Han Wang, Jing Li, Yue Yu, Jie zhou
When reading a story, humans can rapidly understand new fictional characters with a few observations, mainly by drawing analogy to fictional and real people they met before in their lives.
1 code implementation • 30 Oct 2022 • Jiao Ou, Jinchao Zhang, Yang Feng, Jie zhou
The dialogue data admits a wide variety of responses for a given dialogue history, especially responses with different semantics.
no code implementations • 26 Oct 2022 • Jiangnan Li, Mo Yu, Fandong Meng, Zheng Lin, Peng Fu, Weiping Wang, Jie zhou
Although these tasks are effective, there are still urging problems: (1) randomly masking speakers regardless of the question cannot map the speaker mentioned in the question to the corresponding speaker in the dialogue, and ignores the speaker-centric nature of utterances.
1 code implementation • 25 Oct 2022 • Yujia Qin, Cheng Qian, Jing Yi, Weize Chen, Yankai Lin, Xu Han, Zhiyuan Liu, Maosong Sun, Jie zhou
(3) How does the PLM's task knowledge change along the path connecting two minima?
1 code implementation • 24 Oct 2022 • Jing Yi, Weize Chen, Yujia Qin, Yankai Lin, Ning Ding, Xu Han, Zhiyuan Liu, Maosong Sun, Jie zhou
To fathom the mystery, we hypothesize that the adaptations of different DETs could all be reparameterized as low-dimensional optimizations in a unified optimization subspace, which could be found by jointly decomposing independent solutions of different DETs.
1 code implementation • 21 Oct 2022 • Lanrui Wang, Jiangnan Li, Zheng Lin, Fandong Meng, Chenxu Yang, Weiping Wang, Jie zhou
We use a fine-grained encoding strategy which is more sensitive to the emotion dynamics (emotion flow) in the conversations to predict the emotion-intent characteristic of response.
1 code implementation • 18 Oct 2022 • Lan Jiang, Hao Zhou, Yankai Lin, Peng Li, Jie zhou, Rui Jiang
Even though the large-scale language models have achieved excellent performances, they suffer from various adversarial attacks.
3 code implementations • 17 Oct 2022 • Hui Jiang, Ziyao Lu, Fandong Meng, Chulun Zhou, Jie zhou, Degen Huang, Jinsong Su
Meanwhile we inject two types of perturbations into the retrieved pairs for robust training.
no code implementations • 17 Oct 2022 • Zhanqiang Guo, Yao Luan, Jianjiang Feng, Wangsheng Lu, Yin Yin, Guangming Yang, Jie zhou
Accurate cerebrovascular segmentation from Magnetic Resonance Angiography (MRA) and Computed Tomography Angiography (CTA) is of great significance in diagnosis and treatment of cerebrovascular pathology.
1 code implementation • 15 Oct 2022 • An Tao, Yueqi Duan, Yingqi Wang, Jiwen Lu, Jie zhou
To address this issue, we propose a Leaded Gradient Method (LGM) and show the significant effects of the lagged gradient.
no code implementations • COLING 2022 • Yongjing Yin, Yafu Li, Fandong Meng, Jie zhou, Yue Zhang
Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks.
1 code implementation • ICCV 2023 • Han Xiao, Wenzhao Zheng, Zheng Zhu, Jie zhou, Jiwen Lu
Data mixing strategies (e. g., CutMix) have shown the ability to greatly improve the performance of convolutional neural networks (CNNs).
1 code implementation • 11 Oct 2022 • Yuanxin Liu, Fandong Meng, Zheng Lin, Jiangnan Li, Peng Fu, Yanan Cao, Weiping Wang, Jie zhou
In response to the efficiency problem, recent studies show that dense PLMs can be replaced with sparse subnetworks without hurting the performance.
1 code implementation • 11 Oct 2022 • Xiaofeng Zhang, Yikang Shen, Zeyu Huang, Jie zhou, Wenge Rong, Zhang Xiong
This paper proposes the Mixture of Attention Heads (MoA), a new architecture that combines multi-head attention with the MoE mechanism.
1 code implementation • 11 Oct 2022 • Lei LI, Yankai Lin, Xuancheng Ren, Guangxiang Zhao, Peng Li, Jie zhou, Xu sun
We then design a Model Uncertainty--aware Knowledge Integration (MUKI) framework to recover the golden supervision for the student.
1 code implementation • ICCV 2023 • Chengkun Wang, Wenzhao Zheng, Zheng Zhu, Jie zhou, Jiwen Lu
The pretrain-finetune paradigm in modern computer vision facilitates the success of self-supervised learning, which tends to achieve better transferability than supervised learning.
1 code implementation • 10 Oct 2022 • Qingyi Si, Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, Jie zhou
However, these models reveal a trade-off that the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data (which is dominated by the biased samples).
1 code implementation • 10 Oct 2022 • Qingyi Si, Fandong Meng, Mingyu Zheng, Zheng Lin, Yuanxin Liu, Peng Fu, Yanan Cao, Weiping Wang, Jie zhou
To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.
1 code implementation • 9 Oct 2022 • Siyu Lai, Zhen Yang, Fandong Meng, Yufeng Chen, Jinan Xu, Jie zhou
Word alignment which aims to extract lexicon translation equivalents between source and target sentences, serves as a fundamental tool for natural language processing.
no code implementations • 4 Oct 2022 • Jiaju Lin, Jie zhou, Qin Chen
Prompt-based methods have become increasingly popular among information extraction tasks, especially in low-data scenarios.
1 code implementation • COLING 2022 • Shaobin Chen, Jie zhou, Yuling Sun, Liang He
To address this problem, we present an information minimization based contrastive learning (InforMin-CL) model to retain the useful information and discard the redundant information by maximizing the mutual information and minimizing the information entropy between positive instances meanwhile for unsupervised sentence representation learning.