activity
20172022
most citedQuery Resolution for Conversational Search with Limited Supervision

111 citations · 315 across the 19 of their papers we have counts for

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11 papers · 1 filter

cs.CL20221 cited

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

Shanshan Wang, Zhumin Chen, Zhaochun Ren +3

Pre-trained language models (PLM) have demonstrated their effectiveness for a broad range of information retrieval and natural language processing tasks. As the core part of PLM, m…

cs.CL2021

Semi-Supervised Variational Reasoning for Medical Dialogue Generation

Dongdong Li, Zhaochun Ren, Pengjie Ren +4

Medical dialogue generation aims to provide automatic and accurate responses to assist physicians to obtain diagnosis and treatment suggestions in an efficient manner. In medical d…

cs.CL20207 cited

Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and Methodology

Yangjun Zhang, Pengjie Ren, Maarten de Rijke

Conversational interfaces are increasingly popular as a way of connecting people to information. Corpus-based conversational interfaces are able to generate more diverse and natura…

cs.CL20203 cited

Diversifying Task-oriented Dialogue Response Generation with Prototype Guided Paraphrasing

Phillip Lippe, Pengjie Ren, Hinda Haned +2

Existing methods for Dialogue Response Generation (DRG) in Task-oriented Dialogue Systems (TDSs) can be grouped into two categories: template-based and corpus-based. The former pre…

cs.CL202015 cited

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

Minghong Xu, Piji Li, Haoran Yang +4

Unstructured Persona-oriented Dialogue Systems (UPDS) has been demonstrated effective in generating persona consistent responses by utilizing predefined natural language user perso…

cs.CL20195 cited

Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation

Jiahuan Pei, Pengjie Ren, Christof Monz +1

Dialogue response generation (DRG) is a critical component of task-oriented dialogue systems (TDSs). Its purpose is to generate proper natural language responses given some context…