10 code implementations • ICLR 2019 • Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang
We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.
Ranked #2 on Link Prediction on FB122
4 code implementations • 31 Mar 2023 • Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, YiFan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, Ji-Rong Wen
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
1 code implementation • 11 Oct 2023 • Peitian Zhang, Shitao Xiao, Zheng Liu, Zhicheng Dou, Jian-Yun Nie
On the other hand, the task-specific retrievers lack the required versatility, hindering their performance across the diverse retrieval augmentation scenarios.
1 code implementation • 26 Dec 2022 • Tianyi Tang, Junyi Li, Zhipeng Chen, Yiwen Hu, Zhuohao Yu, Wenxun Dai, Zican Dong, Xiaoxue Cheng, Yuhao Wang, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen
To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2. 0, focusing on the use of pre-trained language models (PLMs).
Ranked #1 on Abstractive Text Summarization on CNN/Daily Mail
1 code implementation • 6 Feb 2015 • Wayne Xin Zhao, Xu-Dong Zhang, Daniel Lemire, Dongdong Shan, Jian-Yun Nie, Hongfei Yan, Ji-Rong Wen
Compression algorithms are important for data oriented tasks, especially in the era of Big Data.
2 code implementations • 19 May 2023 • Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen
Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, i. e., content that conflicts with the source or cannot be verified by the factual knowledge.
4 code implementations • 8 Jul 2015 • Alessandro Sordoni, Yoshua Bengio, Hossein Vahabi, Christina Lioma, Jakob G. Simonsen, Jian-Yun Nie
Our novel hierarchical recurrent encoder-decoder architecture allows the model to be sensitive to the order of queries in the context while avoiding data sparsity.
1 code implementation • 12 Apr 2020 • Zhibin Lu, Pan Du, Jian-Yun Nie
Much progress has been made recently on text classification with methods based on neural networks.
1 code implementation • 12 Aug 2019 • Yanru Qu, Ting Bai, Wei-Nan Zhang, Jian-Yun Nie, Jian Tang
This paper studies graph-based recommendation, where an interaction graph is constructed from historical records and is lever-aged to alleviate data sparsity and cold start problems.
Ranked #2 on Click-Through Rate Prediction on MovieLens 1M
1 code implementation • 10 Apr 2023 • Hongjing Qian, Yutao Zhu, Zhicheng Dou, Haoqi Gu, Xinyu Zhang, Zheng Liu, Ruofei Lai, Zhao Cao, Jian-Yun Nie, Ji-Rong Wen
In this paper, we introduce a new NLP task -- generating short factual articles with references for queries by mining supporting evidence from the Web.
1 code implementation • ACL 2020 • Yutao Zhu, Ruihua Song, Zhicheng Dou, Jian-Yun Nie, Jin Zhou
In dialogue systems, it would also be useful to drive dialogues by a dialogue plan.
1 code implementation • 6 Jan 2024 • Junyi Li, Jie Chen, Ruiyang Ren, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen
To tackle the LLM hallucination, three key questions should be well studied: how to detect hallucinations (detection), why do LLMs hallucinate (source), and what can be done to mitigate them (mitigation).
1 code implementation • 24 Oct 2022 • Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen
However, NAR models usually generate texts of lower quality due to the absence of token dependency in the output text.
1 code implementation • 27 Jan 2021 • Yutao Zhu, Kun Zhou, Jian-Yun Nie, Shengchao Liu, Zhicheng Dou
Our experiments on five benchmark datasets show that our method outperforms all the existing baselines significantly, achieving a new state-of-the-art performance.
1 code implementation • NAACL 2022 • Junyi Li, Tianyi Tang, Jian-Yun Nie, Ji-Rong Wen, Wayne Xin Zhao
First, PTG learns a set of source prompts for various source generation tasks and then transfers these prompts as target prompts to perform target generation tasks.
2 code implementations • 24 Oct 2020 • Yan Zeng, Jian-Yun Nie
However, in such a stacked encoder-decoder structure, the operation prediction objective only affects the BERT encoder and the value generation objective mainly affects the RNN decoder.
Ranked #2 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.0
Dialogue State Tracking Multi-domain Dialogue State Tracking
1 code implementation • 24 Aug 2021 • Yutao Zhu, Jian-Yun Nie, Zhicheng Dou, Zhengyi Ma, Xinyu Zhang, Pan Du, Xiaochen Zuo, Hao Jiang
To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences.
1 code implementation • NAACL 2021 • Yan Zeng, Jian-Yun Nie
Conditioned dialogue generation suffers from the scarcity of labeled responses.
1 code implementation • 22 Aug 2022 • Yutao Zhu, Jian-Yun Nie, Yixuan Su, Haonan Chen, Xinyu Zhang, Zhicheng Dou
In this work, we propose a curriculum learning framework for context-aware document ranking, in which the ranking model learns matching signals between the search context and the candidate document in an easy-to-hard manner.
1 code implementation • 25 May 2023 • Fengran Mo, Kelong Mao, Yutao Zhu, Yihong Wu, Kaiyu Huang, Jian-Yun Nie
In this paper, we propose ConvGQR, a new framework to reformulate conversational queries based on generative pre-trained language models (PLMs), one for query rewriting and another for generating potential answers.
1 code implementation • 18 Jul 2021 • Yutao Zhu, Jian-Yun Nie, Kun Zhou, Pan Du, Hao Jiang, Zhicheng Dou
The final response is selected according to the predicted knowledge, the goal to achieve, and the context.
1 code implementation • 20 Oct 2023 • Le Zhang, Yihong Wu, Fengran Mo, Jian-Yun Nie, Aishwarya Agrawal
To enable LLMs to tackle the task in a zero-shot manner, we introduce MoqaGPT, a straightforward and flexible framework.
1 code implementation • 18 May 2023 • Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jingyuan Wang, Jian-Yun Nie, Ji-Rong Wen
In order to further improve the capacity of LLMs for knowledge-intensive tasks, we consider augmenting LLMs with the large-scale web using search engine.
1 code implementation • COLING 2022 • Shangqing Tu, Jifan Yu, Fangwei Zhu, Juanzi Li, Lei Hou, Jian-Yun Nie
However, the trained scoring model is prone to under-fitting for low-resource settings, as it relies on the training data.
1 code implementation • 5 Jun 2023 • Fengran Mo, Jian-Yun Nie, Kaiyu Huang, Kelong Mao, Yutao Zhu, Peng Li, Yang Liu
An effective way to improve retrieval effectiveness is to expand the current query with historical queries.
1 code implementation • 21 Jan 2021 • Yutao Zhu, Jian-Yun Nie, Kun Zhou, Pan Du, Zhicheng Dou
It is thus crucial to select the part of document content relevant to the current conversation context.
1 code implementation • 2 Nov 2023 • Tianyu Zhu, Yansong Shi, Yuan Zhang, Yihong Wu, Fengran Mo, Jian-Yun Nie
Second, we develop a transition-aware embedding distillation module that distills global item-to-item transition patterns into item embeddings, which enables the model to memorize and leverage transitional signals and serves as a calibrator for collaborative signals.
1 code implementation • 30 Jan 2024 • Fengran Mo, Chen Qu, Kelong Mao, Tianyu Zhu, Zhan Su, Kaiyu Huang, Jian-Yun Nie
To address the aforementioned issues, we propose a History-Aware Conversational Dense Retrieval (HAConvDR) system, which incorporates two ideas: context-denoised query reformulation and automatic mining of supervision signals based on the actual impact of historical turns.
1 code implementation • 17 Mar 2024 • Fengran Mo, Bole Yi, Kelong Mao, Chen Qu, Kaiyu Huang, Jian-Yun Nie
Conversational search provides a more convenient interface for users to search by allowing multi-turn interaction with the search engine.
no code implementations • HLT 2015 • Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Margaret Mitchell, Jian-Yun Nie, Jianfeng Gao, Bill Dolan
We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations.
no code implementations • 9 Aug 2018 • Wen-Feng Cheng, Chao-Chung Wu, Ruihua Song, Jianlong Fu, Xing Xie, Jian-Yun Nie
This is one of the few attempts to generate poetry from images.
no code implementations • SEMEVAL 2018 • Pan Du, Jian-Yun Nie
The system aims at exploring the potential of context information of terms for emotion analysis.
no code implementations • ICLR 2018 • Yiping Song, Rui Yan, Cheng-Te Li, Jian-Yun Nie, Ming Zhang, Dongyan Zhao
Human-computer conversation systems have attracted much attention in Natural Language Processing.
no code implementations • 12 Feb 2019 • Ting Bai, Pan Du, Wayne Xin Zhao, Ji-Rong Wen, Jian-Yun Nie
Recommending the right products is the central problem in recommender systems, but the right products should also be recommended at the right time to meet the demands of users, so as to maximize their values.
no code implementations • 19 May 2019 • Zhiqing Sun, Jian Tang, Pan Du, Zhi-Hong Deng, Jian-Yun Nie
Furthermore, we propose a diversified point network to generate a set of diverse keyphrases out of the word graph in the decoding process.
no code implementations • 20 Aug 2019 • Songwei Ge, Zhicheng Dou, Zhengbao Jiang, Jian-Yun Nie, Ji-Rong Wen
Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training.
no code implementations • 21 Oct 2020 • Yan Zeng, Jian-Yun Nie
Existing approaches usually concatenate previous dialogue state with dialogue history as the input to a bi-directional Transformer encoder.
Ranked #5 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.0
Dialogue State Tracking Multi-domain Dialogue State Tracking
no code implementations • 26 Oct 2020 • Zhenzhen Li, Jian-Yun Nie, Benyou Wang, Pan Du, Yuhan Zhang, Lixin Zou, Dongsheng Li
Distant supervision provides a means to create a large number of weakly labeled data at low cost for relation classification.
no code implementations • 24 Oct 2020 • Yan Zeng, Jian-Yun Nie
These results show that discrepancies is an important factor to consider when we use a pre-trained model, and a reduction in discrepancies can lead to improved performance.
no code implementations • 25 Mar 2021 • Yutao Zhu, Jian-Yun Nie, Kun Zhou, Shengchao Liu, Yabo Ling, Pan Du
Sentence ordering aims to arrange the sentences of a given text in the correct order.
no code implementations • NAACL 2021 • Qianqian Xie, Jimin Huang, Pan Du, Min Peng, Jian-Yun Nie
T-VGAE inherits the interpretability of the topic model and the efficient information propagation mechanism of VGAE.
Representation Learning Semi-Supervised Text Classification +1
no code implementations • 28 Jun 2021 • Florian Boudin, Béatrice Daille, Evelyne Jacquey, Jian-Yun Nie
Scientific digital libraries play a critical role in the development and dissemination of scientific literature.
no code implementations • ACL 2021 • Xinying Qiu, Yuan Chen, Hanwu Chen, Jian-Yun Nie, Yuming Shen, Dawei Lu
Deep learning models for automatic readability assessment generally discard linguistic features traditionally used in machine learning models for the task.
no code implementations • 20 Dec 2018 • Yifan Nie, Yanling Li, Jian-Yun Nie
Deep learning models have been employed to perform IR tasks and have shown competitive results.
no code implementations • 14 Jan 2022 • Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen
We begin with introducing three key aspects of applying PLMs to text generation: 1) how to encode the input into representations preserving input semantics which can be fused into PLMs; 2) how to design an effective PLM to serve as the generation model; and 3) how to effectively optimize PLMs given the reference text and to ensure that the generated texts satisfy special text properties.
no code implementations • 25 Jun 2022 • Yihan Wu, Xi Wang, Shaofei Zhang, Lei He, Ruihua Song, Jian-Yun Nie
In this paper, we propose a novel framework for learning style representation from abundant plain text in a self-supervised manner.
no code implementations • 5 Jul 2022 • Pan Du, Jian-Yun Nie, Yutao Zhu, Hao Jiang, Lixin Zou, Xiaohui Yan
Beyond topical relevance, passage ranking for open-domain factoid question answering also requires a passage to contain an answer (answerability).
no code implementations • Findings (NAACL) 2022 • Li Zhenzhen, Yuyang Zhang, Jian-Yun Nie, Dongsheng Li
In this paper, we propose to learn a prototype encoder from relation definition in a way that is useful for relation instance classification.
no code implementations • 24 Nov 2022 • Xinying Qiu, Shuxuan Liao, Jiajun Xie, Jian-Yun Nie
In this paper, we propose a novel approach to extract and represent essay coherence features with prompt-learning NSP that shows to match the state-of-the-art AES coherence model, and achieves the best performance for long essays.
no code implementations • 11 Jan 2023 • Nam Le Hai, Thomas Gerald, Thibault Formal, Jian-Yun Nie, Benjamin Piwowarski, Laure Soulier
Conversational search is a difficult task as it aims at retrieving documents based not only on the current user query but also on the full conversation history.
no code implementations • 25 Apr 2023 • Junyi Li, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen
In this way, conditional text generation can be cast as a glyph image generation task, and it is then natural to apply continuous diffusion models to discrete texts.
1 code implementation • 10 May 2023 • Zhibin Lu, Qianqian Xie, Benyou Wang, Jian-Yun Nie
An inductive Word-grounded Graph Convolutional Network (WGCN) is proposed to learn word and document representations based on WGraph in a supervised manner.
no code implementations • 10 Nov 2023 • Pierre Erbacher, Jian-Yun Nie, Philippe Preux, Laure Soulier
The only two datasets known to us that contain both document relevance judgments and the associated clarification interactions are Qulac and ClariQ.
1 code implementation • 18 Feb 2024 • Yujia Zhou, Zheng Liu, Jiajie Jin, Jian-Yun Nie, Zhicheng Dou
Drawing from cognitive psychology, metacognition allows an entity to self-reflect and critically evaluate its cognitive processes.
no code implementations • 26 Feb 2024 • Pierre Erbacher, Jian-Yun Nie, Philippe Preux, Laure Soulier
Conversational systems have made significant progress in generating natural language responses.