Search Results for author: Zhaochun Ren

Found 35 papers, 22 papers with code

Event Transition Planning for Open-ended Text Generation

no code implementations Findings (ACL) 2022 Qintong Li, Piji Li, Wei Bi, Zhaochun Ren, Yuxuan Lai, Lingpeng Kong

Open-ended text generation tasks, such as dialogue generation and story completion, require models to generate a coherent continuation given limited preceding context.

Dialogue Generation Story Completion

Paying More Attention to Self-attention: Improving Pre-trained Language Models via Attention Guiding

no code implementations6 Apr 2022 Shanshan Wang, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Qiang Yan, Pengjie Ren

In this work, we propose a simple yet effective attention guiding mechanism to improve the performance of PLM by encouraging attention towards the established goals.

Information Retrieval

Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems

no code implementations2 Apr 2022 Weiwei Sun, Shuyu Guo, Shuo Zhang, Pengjie Ren, Zhumin Chen, Maarten de Rijke, Zhaochun Ren

Employing existing user simulators to evaluate TDSs is challenging as user simulators are primarily designed to optimize dialogue policies for TDSs and have limited evaluation capability.

Task-Oriented Dialogue Systems

Membership Inference Attacks Against Recommender Systems

1 code implementation16 Sep 2021 Minxing Zhang, Zhaochun Ren, Zihan Wang, Pengjie Ren, Zhumin Chen, Pengfei Hu, Yang Zhang

In this paper, we make the first attempt on quantifying the privacy leakage of recommender systems through the lens of membership inference.

Recommendation Systems

ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues

1 code implementation1 Sep 2021 Guojun Yan, Jiahuan Pei, Pengjie Ren, Zhaochun Ren, Xin Xin, Huasheng Liang, Maarten de Rijke, Zhumin Chen

(1) there is no dataset with large-scale medical dialogues that covers multiple medical services and contains fine-grained medical labels (i. e., intents, actions, slots, values), and (2) there is no set of established benchmarks for MDSs for multi-domain, multi-service medical dialogues.

Contrastive Learning Dialogue Generation +1

Learning to Ask Conversational Questions by Optimizing Levenshtein Distance

1 code implementation ACL 2021 Zhongkun Liu, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Maarten de Rijke, Ming Zhou

Conversational Question Simplification (CQS) aims to simplify self-contained questions into conversational ones by incorporating some conversational characteristics, e. g., anaphora and ellipsis.

Few-Shot Electronic Health Record Coding through Graph Contrastive Learning

1 code implementation29 Jun 2021 Shanshan Wang, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Qiang Yan, Evangelos Kanoulas, Maarten de Rijke

We seek to improve the performance for both frequent and rare ICD codes by using a contrastive graph-based EHR coding framework, CoGraph, which re-casts EHR coding as a few-shot learning task.

Contrastive Learning Few-Shot Learning

Improving Transformer-based Sequential Recommenders through Preference Editing

no code implementations23 Jun 2021 Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Jun Ma, Maarten de Rijke

By doing so, the SR model is able to learn how to identify common and unique user preferences, and thereby do better user preference extraction and representation.

Self-Supervised Learning Sequential Recommendation

Wizard of Search Engine: Access to Information Through Conversations with Search Engines

1 code implementation18 May 2021 Pengjie Ren, Zhongkun Liu, Xiaomeng Song, Hongtao Tian, Zhumin Chen, Zhaochun Ren, Maarten de Rijke

(2) We release a benchmark dataset, called wizard of search engine (WISE), which allows for comprehensive and in-depth research on all aspects of CIS.

Intent Detection Keyphrase Extraction +1

Abstractive Opinion Tagging

1 code implementation18 Jan 2021 Qintong Li, Piji Li, Xinyi Li, Zhaochun Ren, Zhumin Chen, Maarten de Rijke

In this paper, we propose the abstractive opinion tagging task, where systems have to automatically generate a ranked list of opinion tags that are based on, but need not occur in, a given set of user-generated reviews.

EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation

1 code implementation COLING 2020 Qintong Li, Hongshen Chen, Zhaochun Ren, Pengjie Ren, Zhaopeng Tu, Zhumin Chen

In response to this problem, we propose a multi-resolution adversarial model {--} EmpDG, to generate more empathetic responses.

Dialogue Generation

Mixed Information Flow for Cross-domain Sequential Recommendations

1 code implementation1 Dec 2020 Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Lifan Zhao, Jun Ma, Maarten de Rijke

One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains.

Sequential Recommendation Transfer Learning

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

Knowledge Bridging for Empathetic Dialogue Generation

1 code implementation21 Sep 2020 Qintong Li, Piji Li, Zhaochun Ren, Pengjie Ren, Zhumin Chen

Finally, to generate the empathetic response, we propose an emotional cross-attention mechanism to learn the emotional dependencies from the emotional context graph.

Dialogue Generation

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

Conversations with Search Engines: SERP-based Conversational Response Generation

1 code implementation29 Apr 2020 Pengjie Ren, Zhumin Chen, Zhaochun Ren, Evangelos Kanoulas, Christof Monz, Maarten de Rijke

In this paper, we address the problem of answering complex information needs by conversing conversations with search engines, in the sense that users can express their queries in natural language, and directly receivethe information they need from a short system response in a conversational manner.

Conversational Response Generation Conversational Search +1

A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation

2 code implementations6 Feb 2020 Minghong Xu, Piji Li, Haoran Yang, Pengjie Ren, Zhaochun Ren, Zhumin Chen, Jun Ma

To address this, we propose a neural topical expansion framework, namely Persona Exploration and Exploitation (PEE), which is able to extend the predefined user persona description with semantically correlated content before utilizing them to generate dialogue responses.

Dialogue Generation

EmpDG: Multiresolution Interactive Empathetic Dialogue Generation

1 code implementation20 Nov 2019 Qintong Li, Hongshen Chen, Zhaochun Ren, Pengjie Ren, Zhaopeng Tu, Zhumin Chen

In response to this problem, we propose a multi-resolution adversarial model -- EmpDG, to generate more empathetic responses.

Dialogue Generation

Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations

no code implementations6 Oct 2019 Wenchao Sun, Muyang Ma, Pengjie Ren, Yujie Lin, Zhumin Chen, Zhaochun Ren, Jun Ma, Maarten de Rijke

We study sequential recommendation in a particularly challenging context, in which multiple individual users share asingle account (i. e., they have a shared account) and in which user behavior is available in multiple domains (i. e., recommendations are cross-domain).

Sequential Recommendation

Improving Outfit Recommendation with Co-supervision of Fashion Generation

no code implementations24 Aug 2019 Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, Maarten de Rijke

FARM improves visual understanding by incorporating the supervision of generation loss, which we hypothesize to be able to better encode aesthetic information.

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

RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation

1 code implementation6 Dec 2018 Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, Maarten de Rijke

RepeatNet integrates a regular neural recommendation approach in the decoder with a new repeat recommendation mechanism that can choose items from a user's history and recommends them at the right time.

Session-Based Recommendations

Streaming Graph Neural Networks

2 code implementations24 Oct 2018 Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin

Current graph neural network models cannot utilize the dynamic information in dynamic graphs.

Community Detection General Classification +3

Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation

2 code implementations31 Aug 2018 Xisen Jin, Wenqiang Lei, Zhaochun Ren, Hongshen Chen, Shangsong Liang, Yihong Zhao, Dawei Yin

However, the \emph{expensive nature of state labeling} and the \emph{weak interpretability} make the dialogue state tracking a challenging problem for both task-oriented and non-task-oriented dialogue generation: For generating responses in task-oriented dialogues, state tracking is usually learned from manually annotated corpora, where the human annotation is expensive for training; for generating responses in non-task-oriented dialogues, most of existing work neglects the explicit state tracking due to the unlimited number of dialogue states.

Dialogue Generation Dialogue State Tracking

Knowledge Diffusion for Neural Dialogue Generation

1 code implementation ACL 2018 Shuman Liu, Hongshen Chen, Zhaochun Ren, Yang Feng, Qun Liu, Dawei Yin

Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats.

Dialogue Generation Question Answering +1

Neural Attentive Session-based Recommendation

3 code implementations13 Nov 2017 Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma

Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later.

Session-Based Recommendations

Neural Att entive Session-based Recommendation

1 code implementation CIKM 2017 Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, Jun Ma

Specifically, we explore a hybrid encoder with an attention mechanism to model the user’s sequential behavior and capture the user’s main purpose in the current session, which are combined as a unified session representation later.

Session-Based Recommendations

Neural Rating Regression with Abstractive Tips Generation for Recommendation

no code implementations1 Aug 2017 Piji Li, ZiHao Wang, Zhaochun Ren, Lidong Bing, Wai Lam

In essence, writing some tips and giving a numerical rating are two facets of a user's product assessment action, expressing the user experience and feelings.

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