Search Results for author: Jia-Chen Gu

Found 17 papers, 14 papers with code

Conversation- and Tree-Structure Losses for Dialogue Disentanglement

no code implementations dialdoc (ACL) 2022 Tianda Li, Jia-Chen Gu, Zhen-Hua Ling, Quan Liu

When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately.

Disentanglement

HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations

1 code implementation ACL 2022 Jia-Chen Gu, Chao-Hong Tan, Chongyang Tao, Zhen-Hua Ling, Huang Hu, Xiubo Geng, Daxin Jiang

To address these challenges, we present HeterMPC, a heterogeneous graph-based neural network for response generation in MPCs which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph.

Response Generation

Detecting Speaker Personas from Conversational Texts

1 code implementation EMNLP 2021 Jia-Chen Gu, Zhen-Hua Ling, Yu Wu, Quan Liu, Zhigang Chen, Xiaodan Zhu

This is a many-to-many semantic matching task because both contexts and personas in SPD are composed of multiple sentences.

MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding

1 code implementation ACL 2021 Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling, Can Xu, Xiubo Geng, Daxin Jiang

Recently, various neural models for multi-party conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification and response prediction.

Language Modelling Speaker Identification

Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots

1 code implementation19 May 2021 Jia-Chen Gu, Hui Liu, Zhen-Hua Ling, Quan Liu, Zhigang Chen, Xiaodan Zhu

Empirical studies on the Persona-Chat dataset show that the partner personas neglected in previous studies can improve the accuracy of response selection in the IMN- and BERT-based models.

Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems

1 code implementation22 Dec 2020 Chao-Hong Tan, Xiaoyu Yang, Zi'ou Zheng, Tianda Li, Yufei Feng, Jia-Chen Gu, Quan Liu, Dan Liu, Zhen-Hua Ling, Xiaodan Zhu

Task-oriented conversational modeling with unstructured knowledge access, as track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to build a system to generate response given dialogue history and knowledge access.

Response Generation Task-Oriented Dialogue Systems

Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots

1 code implementation Findings of the Association for Computational Linguistics 2020 Jia-Chen Gu, Zhen-Hua Ling, Quan Liu, Zhigang Chen, Xiaodan Zhu

The challenges of building knowledge-grounded retrieval-based chatbots lie in how to ground a conversation on its background knowledge and how to match response candidates with both context and knowledge simultaneously.

DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement

1 code implementation8 Apr 2020 Tianda Li, Jia-Chen Gu, Xiaodan Zhu, Quan Liu, Zhen-Hua Ling, Zhiming Su, Si Wei

Disentanglement is a problem in which multiple conversations occur in the same channel simultaneously, and the listener should decide which utterance is part of the conversation he will respond to.

Conversation Disentanglement Disentanglement

Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots

1 code implementation IJCNLP 2019 Jia-Chen Gu, Zhen-Hua Ling, Xiaodan Zhu, Quan Liu

Compared with previous persona fusion approaches which enhance the representation of a context by calculating its similarity with a given persona, the DIM model adopts a dual matching architecture, which performs interactive matching between responses and contexts and between responses and personas respectively for ranking response candidates.

Promoting Diversity for End-to-End Conversation Response Generation

no code implementations27 Jan 2019 Yu-Ping Ruan, Zhen-Hua Ling, Quan Liu, Jia-Chen Gu, Xiaodan Zhu

At this stage, two different models are proposed, i. e., a variational generative (VariGen) model and a retrieval based (Retrieval) model.

Response Generation

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