Search Results for author: Chao-Hong Tan

Found 8 papers, 7 papers with code

Is ChatGPT a Good Multi-Party Conversation Solver?

1 code implementation25 Oct 2023 Chao-Hong Tan, Jia-Chen Gu, Zhen-Hua Ling

Large Language Models (LLMs) have emerged as influential instruments within the realm of natural language processing; nevertheless, their capacity to handle multi-party conversations (MPCs) -- a scenario marked by the presence of multiple interlocutors involved in intricate information exchanges -- remains uncharted.

Zero-Shot Learning

MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation

1 code implementation22 May 2023 Jia-Chen Gu, Chao-Hong Tan, Caiyuan Chu, Zhen-Hua Ling, Chongyang Tao, Quan Liu, Cong Liu

Given an MPC with a few addressee labels missing, existing methods fail to build a consecutively connected conversation graph, but only a few separate conversation fragments instead.

DiffuSIA: A Spiral Interaction Architecture for Encoder-Decoder Text Diffusion

no code implementations19 May 2023 Chao-Hong Tan, Jia-Chen Gu, Zhen-Hua Ling

In fact, the encoder-decoder architecture is naturally more flexible for its detachable encoder and decoder modules, which is extensible to multilingual and multimodal generation tasks for conditions and target texts.

Conditional Text Generation Dialogue Generation +4

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

Neural Grapheme-to-Phoneme Conversion with Pre-trained Grapheme Models

1 code implementation26 Jan 2022 Lu Dong, Zhi-Qiang Guo, Chao-Hong Tan, Ya-Jun Hu, Yuan Jiang, Zhen-Hua Ling

Neural network models have achieved state-of-the-art performance on grapheme-to-phoneme (G2P) conversion.

Language Modelling

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

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