Search Results for author: Dongyan Zhao

Found 106 papers, 38 papers with code

Combining Impression Feature Representation for Multi-turn Conversational Question Answering

no code implementations CCL 2020 Shaoling Jing, Shibo Hong, Dongyan Zhao, Haihua Xie, Zhi Tang

Multi-turn conversational Question Answering (ConvQA) is a practical task that requires the understanding of conversation history, such as previous QA pairs, the passage context, and current question.

Conversational Question Answering feature selection

ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation

no code implementations ACL 2022 Chang Liu, Xu Tan, Chongyang Tao, Zhenxin Fu, Dongyan Zhao, Tie-Yan Liu, Rui Yan

To enable the chatbot to foresee the dialogue future, we design a beam-search-like roll-out strategy for dialogue future simulation using a typical dialogue generation model and a dialogue selector.

Dialogue Generation Response Generation

Finding the Dominant Winning Ticket in Pre-Trained Language Models

no code implementations Findings (ACL) 2022 Zhuocheng Gong, Di He, Yelong Shen, Tie-Yan Liu, Weizhu Chen, Dongyan Zhao, Ji-Rong Wen, Rui Yan

Empirically, we show that (a) the dominant winning ticket can achieve performance that is comparable with that of the full-parameter model, (b) the dominant winning ticket is transferable across different tasks, (c) and the dominant winning ticket has a natural structure within each parameter matrix.

Understanding Procedural Text using Interactive Entity Networks

no code implementations EMNLP 2020 Jizhi Tang, Yansong Feng, Dongyan Zhao

Recent efforts have made great progress to track multiple entities in a procedural text, but usually treat each entity separately and ignore the fact that there are often multiple entities interacting with each other during one process, some of which are even explicitly mentioned.

Reading Comprehension

Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation

no code implementations Findings (EMNLP) 2021 Qingqing Zhu, Xiuying Chen, Pengfei Wu, Junfei Liu, Dongyan Zhao

Hence, in this paper, we introduce a combination of curriculum learning and knowledge distillation for efficient dialogue generation models, where curriculum learning can help knowledge distillation from data and model aspects.

Dialogue Generation Knowledge Distillation

Plan-CVAE: A Planning-based Conditional Variational Autoencoder for Story Generation

no code implementations CCL 2020 Lin Wang, Juntao Li, Rui Yan, Dongyan Zhao

Story generation is a challenging task of automatically creating natural languages to describe a sequence of events, which requires outputting text with not only a consistent topic but also novel wordings.

Story Generation

GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Passage Retrieval

no code implementations18 Apr 2022 Jiduan Liu, Jiahao Liu, Yang Yang, Jingang Wang, Wei Wu, Dongyan Zhao, Rui Yan

To enhance the performance of dense retrieval models without loss of efficiency, we propose a GNN-encoder model in which query (passage) information is fused into passage (query) representations via graph neural networks that are constructed by queries and their top retrieved passages.

Natural Questions Passage Retrieval +1

Learning to Express in Knowledge-Grounded Conversation

no code implementations NAACL 2022 Xueliang Zhao, Tingchen Fu, Chongyang Tao, Wei Wu, Dongyan Zhao, Rui Yan

Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses.

Dialogue Generation

Entailment Graph Learning with Textual Entailment and Soft Transitivity

1 code implementation ACL 2022 Zhibin Chen, Yansong Feng, Dongyan Zhao

Typed entailment graphs try to learn the entailment relations between predicates from text and model them as edges between predicate nodes.

Graph Learning Natural Language Inference

Things not Written in Text: Exploring Spatial Commonsense from Visual Signals

1 code implementation ACL 2022 Xiao Liu, Da Yin, Yansong Feng, Dongyan Zhao

We probe PLMs and models with visual signals, including vision-language pretrained models and image synthesis models, on this benchmark, and find that image synthesis models are more capable of learning accurate and consistent spatial knowledge than other models.

Image Generation Natural Language Understanding +1

Stylized Dialogue Generation with Multi-Pass Dual Learning

1 code implementation NeurIPS 2021 Jinpeng Li, Yingce Xia, Rui Yan, Hongda Sun, Dongyan Zhao, Tie-Yan Liu

Considering there is no parallel data between the contexts and the responses of target style S1, existing works mainly use back translation to generate stylized synthetic data for training, where the data about context, target style S1 and an intermediate style S0 is used.

Dialogue Generation

Extract, Integrate, Compete: Towards Verification Style Reading Comprehension

1 code implementation Findings (EMNLP) 2021 Chen Zhang, Yuxuan Lai, Yansong Feng, Dongyan Zhao

In this paper, we present a new verification style reading comprehension dataset named VGaokao from Chinese Language tests of Gaokao.

Reading Comprehension

Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues

1 code implementation ACM Transactions on Information Systems 2021 Ruijian Xu, Chongyang Tao, Jiazhan Feng, Wei Wu, Rui Yan, Dongyan Zhao

To tackle these challenges, we propose a representation[K]-interaction[L]-matching framework that explores multiple types of deep interactive representations to build context-response matching models for response selection.

Conversational Response Selection

Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation

1 code implementation ACL 2021 Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Xiangliang Zhang, Dongyan Zhao, Rui Yan

Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area.

Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction

1 code implementation ACL 2021 Quzhe Huang, Shengqi Zhu, Yansong Feng, Yuan Ye, Yuxuan Lai, Dongyan Zhao

Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences.

Relation Extraction

Exploring Distantly-Labeled Rationales in Neural Network Models

no code implementations ACL 2021 Quzhe Huang, Shengqi Zhu, Yansong Feng, Dongyan Zhao

Recent studies strive to incorporate various human rationales into neural networks to improve model performance, but few pay attention to the quality of the rationales.

Why Machine Reading Comprehension Models Learn Shortcuts?

1 code implementation Findings (ACL) 2021 Yuxuan Lai, Chen Zhang, Yansong Feng, Quzhe Huang, Dongyan Zhao

A thorough empirical analysis shows that MRC models tend to learn shortcut questions earlier than challenging questions, and the high proportions of shortcut questions in training sets hinder models from exploring the sophisticated reasoning skills in the later stage of training.

Machine Reading Comprehension

Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study

no code implementations NAACL 2021 Chongyang Tao, Shen Gao, Juntao Li, Yansong Feng, Dongyan Zhao, Rui Yan

Sequential information, a. k. a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders.

Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models

2 code implementations NAACL 2021 Yuxuan Lai, Yijia Liu, Yansong Feng, Songfang Huang, Dongyan Zhao

Further analysis shows that Lattice-BERT can harness the lattice structures, and the improvement comes from the exploration of redundant information and multi-granularity representations.

Natural Language Understanding Pretrained Language Models

Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots

no code implementations17 Mar 2021 Juntao Li, Chang Liu, Chongyang Tao, Zhangming Chan, Dongyan Zhao, Min Zhang, Rui Yan

To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN).

Representation Learning

Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension

1 code implementation14 Dec 2020 Xiuying Chen, Zhi Cui, Jiayi Zhang, Chen Wei, Jianwei Cui, Bin Wang, Dongyan Zhao, Rui Yan

Hence, in this paper, we propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question, where the question is focused on the omitted information in the dialog.

Multi-Task Learning Reading Comprehension +1

The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement

no code implementations14 Dec 2020 Mingzhe Li, Xiuying Chen, Min Yang, Shen Gao, Dongyan Zhao, Rui Yan

In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style.


Meaningful Answer Generation of E-Commerce Question-Answering

no code implementations14 Nov 2020 Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan

To generate more meaningful answers, in this paper, we propose a novel generative neural model, called the Meaningful Product Answer Generator (MPAG), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration.

Answer Generation Question Answering +1

Learning to Respond with Your Favorite Stickers: A Framework of Unifying Multi-Modality and User Preference in Multi-Turn Dialog

no code implementations5 Nov 2020 Shen Gao, Xiuying Chen, Li Liu, Dongyan Zhao, Rui Yan

Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context and sticker using history of user.

Towards Context-Aware Code Comment Generation

no code implementations Findings of the Association for Computational Linguistics 2020 Xiaohan Yu, Quzhe Huang, Zheng Wang, Yansong Feng, Dongyan Zhao

Code comments are vital for software maintenance and comprehension, but many software projects suffer from the lack of meaningful and up-to-date comments in practice.

Code Comment Generation Graph Attention

VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles

1 code implementation EMNLP 2020 Mingzhe Li, Xiuying Chen, Shen Gao, Zhangming Chan, Dongyan Zhao, Rui Yan

Hence, in this paper, we propose the task of Video-based Multimodal Summarization with Multimodal Output (VMSMO) to tackle such a problem.

Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues

no code implementations14 Sep 2020 Ruijian Xu, Chongyang Tao, Daxin Jiang, Xueliang Zhao, Dongyan Zhao, Rui Yan

To address these issues, in this paper, we propose learning a context-response matching model with auxiliary self-supervised tasks designed for the dialogue data based on pre-trained language models.

Conversational Response Selection

Domain Adaptation for Semantic Parsing

no code implementations23 Jun 2020 Zechang Li, Yuxuan Lai, Yansong Feng, Dongyan Zhao

In this paper, we propose a novel semantic parser for domain adaptation, where we have much fewer annotated data in the target domain compared to the source domain.

Domain Adaptation Semantic Parsing

Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce

1 code implementation17 May 2020 Juntao Li, Chang Liu, Jian Wang, Lidong Bing, Hongsong Li, Xiaozhong Liu, Dongyan Zhao, Rui Yan

We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.

Cross-Lingual Information Retrieval

Neighborhood Matching Network for Entity Alignment

1 code implementation ACL 2020 Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao

This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge.

Entity Alignment Graph Sampling +1

From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information

no code implementations10 May 2020 Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan

Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words.

Text Summarization

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

1 code implementation10 Mar 2020 Shen Gao, Xiuying Chen, Chang Liu, Li Liu, Dongyan Zhao, Rui Yan

Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances.

Low-Resource Knowledge-Grounded Dialogue Generation

no code implementations ICLR 2020 Xueliang Zhao, Wei Wu, Chongyang Tao, Can Xu, Dongyan Zhao, Rui Yan

In such a low-resource setting, we devise a disentangled response decoder in order to isolate parameters that depend on knowledge-grounded dialogues from the entire generation model.

Dialogue Generation Response Generation

Integrating Relation Constraints with Neural Relation Extractors

1 code implementation26 Nov 2019 Yuan Ye, Yansong Feng, Bingfeng Luo, Yuxuan Lai, Dongyan Zhao

However, such models often make predictions for each entity pair individually, thus often fail to solve the inconsistency among different predictions, which can be characterized by discrete relation constraints.

Relation Extraction

Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce

1 code implementation7 Nov 2019 Zhenxin Fu, Feng Ji, Wenpeng Hu, Wei Zhou, Dongyan Zhao, Haiqing Chen, Rui Yan

Information-seeking conversation system aims at satisfying the information needs of users through conversations.

Text Matching

Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation

no code implementations IJCNLP 2019 Zhangming Chan, Xiuying Chen, Yongliang Wang, Juntao Li, Zhiqiang Zhang, Kun Gai, Dongyan Zhao, Rui Yan

Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information.

Text Generation

Learning to Update Knowledge Graphs by Reading News

no code implementations IJCNLP 2019 Jizhi Tang, Yansong Feng, Dongyan Zhao

News streams contain rich up-to-date information which can be used to update knowledge graphs (KGs).

Knowledge Graphs

Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations

no code implementations IJCNLP 2019 Ran Le, Wenpeng Hu, Mingyue Shang, Zhenjun You, Lidong Bing, Dongyan Zhao, Rui Yan

Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario.

RPM-Oriented Query Rewriting Framework for E-commerce Keyword-Based Sponsored Search

no code implementations28 Oct 2019 Xiuying Chen, Daorui Xiao, Shen Gao, Guojun Liu, Wei. Lin, Bo Zheng, Dongyan Zhao, Rui Yan

Sponsored search optimizes revenue and relevance, which is estimated by Revenue Per Mille (RPM).

Multilingual Dialogue Generation with Shared-Private Memory

no code implementations6 Oct 2019 Chen Chen, Lisong Qiu, Zhenxin Fu, Dongyan Zhao, Junfei Liu, Rui Yan

Existing dialog systems are all monolingual, where features shared among different languages are rarely explored.

Cross-Lingual Transfer Dialogue Generation

Learning from Positive and Unlabeled Data with Adversarial Training

no code implementations25 Sep 2019 Wenpeng Hu, Ran Le, Bing Liu, Feng Ji, Haiqing Chen, Dongyan Zhao, Jinwen Ma, Rui Yan

Positive-unlabeled (PU) learning learns a binary classifier using only positive and unlabeled examples without labeled negative examples.

Semi-supervised Text Style Transfer: Cross Projection in Latent Space

no code implementations IJCNLP 2019 Mingyue Shang, Piji Li, Zhenxin Fu, Lidong Bing, Dongyan Zhao, Shuming Shi, Rui Yan

Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data.

Style Transfer Text Style Transfer

Jointly Learning Entity and Relation Representations for Entity Alignment

1 code implementation IJCNLP 2019 Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao

Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge graphs (KGs).

Ranked #10 on Entity Alignment on DBP15k zh-en (using extra training data)

Entity Alignment Entity Embeddings +1

How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing

1 code implementation IJCNLP 2019 Shen Gao, Xiuying Chen, Piji Li, Zhangming Chan, Dongyan Zhao, Rui Yan

There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words---such as irrelevant facts---into the generated summaries.

Abstractive Text Summarization

A Sketch-Based System for Semantic Parsing

1 code implementation2 Sep 2019 Zechang Li, Yuxuan Lai, Yuxi Xie, Yansong Feng, Dongyan Zhao

The sketch is a high-level structure of the logical form exclusive of low-level details such as entities and predicates.

Semantic Parsing

Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs

1 code implementation22 Aug 2019 Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, Dongyan Zhao

Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods.

Ranked #12 on Entity Alignment on DBP15k zh-en (using extra training data)

Entity Alignment Entity Embeddings +1

Learning towards Abstractive Timeline Summarization

1 code implementation IJCAI 2019 2019 Xiuying Chen, Zhangming Chan, Shen Gao, Meng-Hsuan Yu, Dongyan Zhao, Rui Yan

Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods. In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, we propose a memory-based timeline summarization model (MTS). Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process. Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events. Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations.

Document Summarization Timeline Summarization +1

Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References

no code implementations ACL 2019 Lisong Qiu, Juntao Li, Wei Bi, Dongyan Zhao, Rui Yan

Due to its potential applications, open-domain dialogue generation has become popular and achieved remarkable progress in recent years, but sometimes suffers from generic responses.

Dialogue Generation

A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots

no code implementations11 Jun 2019 Xueliang Zhao, Chongyang Tao, Wei Wu, Can Xu, Dongyan Zhao, Rui Yan

We present a document-grounded matching network (DGMN) for response selection that can power a knowledge-aware retrieval-based chatbot system.


GSN: A Graph-Structured Network for Multi-Party Dialogues

1 code implementation31 May 2019 Wenpeng Hu, Zhangming Chan, Bing Liu, Dongyan Zhao, Jinwen Ma, Rui Yan

Existing neural models for dialogue response generation assume that utterances are sequentially organized.

Response Generation

Lattice CNNs for Matching Based Chinese Question Answering

1 code implementation25 Feb 2019 Yuxuan Lai, Yansong Feng, Xiaohan Yu, Zheng Wang, Kun Xu, Dongyan Zhao

Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages like Chinese where there is no natural space to segment words explicitly.

Question Answering Text Matching

Product-Aware Answer Generation in E-Commerce Question-Answering

1 code implementation23 Jan 2019 Shen Gao, Zhaochun Ren, Yihong Eric Zhao, Dongyan Zhao, Dawei Yin, Rui Yan

In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes.

Answer Generation Question Answering

Abstractive Text Summarization by Incorporating Reader Comments

no code implementations13 Dec 2018 Shen Gao, Xiuying Chen, Piji Li, Zhaochun Ren, Lidong Bing, Dongyan Zhao, Rui Yan

To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect.

Reader-Aware Summarization

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

no code implementations13 Dec 2018 Mingyue Shang, Zhenxin Fu, Hongzhi Yin, Bo Tang, Dongyan Zhao, Rui Yan

In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT).

Cloze Test Natural Language Inference +1

Chat More If You Like: Dynamic Cue Words Planning to Flow Longer Conversations

no code implementations19 Nov 2018 Lili Yao, Ruijian Xu, Chao Li, Dongyan Zhao, Rui Yan

To build an open-domain multi-turn conversation system is one of the most interesting and challenging tasks in Artificial Intelligence.

Plan-And-Write: Towards Better Automatic Storytelling

2 code implementations14 Nov 2018 Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, Rui Yan

Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events.

Story Generation

Encoding Implicit Relation Requirements for Relation Extraction: A Joint Inference Approach

no code implementations9 Nov 2018 Li-Wei Chen, Yansong Feng, Songfang Huang, Bingfeng Luo, Dongyan Zhao

Relation extraction is the task of identifying predefined relationship between entities, and plays an essential role in information extraction, knowledge base construction, question answering and so on.

Question Answering Relation Extraction

Iterative Document Representation Learning Towards Summarization with Polishing

1 code implementation EMNLP 2018 Xiuying Chen, Shen Gao, Chongyang Tao, Yan Song, Dongyan Zhao, Rui Yan

In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents.

Extractive Text Summarization Representation Learning

Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection

no code implementations22 Aug 2018 Chongyang Tao, Wei Wu, Can Xu, Yansong Feng, Dongyan Zhao, Rui Yan

In this paper, we study context-response matching with pre-trained contextualized representations for multi-turn response selection in retrieval-based chatbots.

Dialogue Generation

Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding

no code implementations ACL 2018 Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, Dongyan Zhao

The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data.

Intent Detection Natural Language Processing +2

Tree2Tree Learning with Memory Unit

no code implementations ICLR 2018 Ning Miao, Hengliang Wang, Ran Le, Chongyang Tao, Mingyue Shang, Rui Yan, Dongyan Zhao

Traditional recurrent neural network (RNN) or convolutional neural net- work (CNN) based sequence-to-sequence model can not handle tree structural data well.

Machine Translation Translation

Topic-Based Question Generation

no code implementations ICLR 2018 Wenpeng Hu, Bing Liu, Rui Yan, Dongyan Zhao, Jinwen Ma

In the paper, we propose a new question generation problem, which also requires the input of a target topic in addition to a piece of descriptive text.

Chatbot Question Answering +1

Scale Up Event Extraction Learning via Automatic Training Data Generation

no code implementations11 Dec 2017 Ying Zeng, Yansong Feng, Rong Ma, Zheng Wang, Rui Yan, Chongde Shi, Dongyan Zhao

We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events.

Event Extraction

Style Transfer in Text: Exploration and Evaluation

2 code implementations18 Nov 2017 Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, Rui Yan

Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.

Natural Language Processing Style Transfer +1

Diversifying Neural Conversation Model with Maximal Marginal Relevance

no code implementations IJCNLP 2017 Yiping Song, Zhiliang Tian, Dongyan Zhao, Ming Zhang, Rui Yan

However, traditional seq2seq suffer from a severe weakness: during beam search decoding, they tend to rank universal replies at the top of the candidate list, resulting in the lack of diversity among candidate replies.

Document Summarization Information Retrieval +1

Learning to Predict Charges for Criminal Cases with Legal Basis

no code implementations EMNLP 2017 Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang, Dongyan Zhao

The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description.

A Constrained Sequence-to-Sequence Neural Model for Sentence Simplification

no code implementations7 Apr 2017 Yaoyuan Zhang, Zhenxu Ye, Yansong Feng, Dongyan Zhao, Rui Yan

For word-level studies, words are simplified but also have potential grammar errors due to different usages of words before and after simplification.

RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems

1 code implementation11 Jan 2017 Chongyang Tao, Lili Mou, Dongyan Zhao, Rui Yan

Open-domain human-computer conversation has been attracting increasing attention over the past few years.

Dialogue Evaluation

Hybrid Question Answering over Knowledge Base and Free Text

no code implementations COLING 2016 Kun Xu, Yansong Feng, Songfang Huang, Dongyan Zhao

While these systems are able to provide more precise answers than information retrieval (IR) based QA systems, the natural incompleteness of KB inevitably limits the question scope that the system can answer.

Information Retrieval Question Answering +1

Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems

2 code implementations23 Oct 2016 Yiping Song, Rui Yan, Xiang Li, Dongyan Zhao, Ming Zhang

In this paper, we propose a novel ensemble of retrieval-based and generation-based dialog systems in the open domain.

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