research-article

Learning Multi-turn Response Selection in Grounded Dialogues with Reinforced Knowledge and Context Distillation

Authors:

Dongyan ZhaoAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 4

Article No.: 115, Pages 1 - 27

https://doi.org/10.1145/3584701

Published: 21 April 2023 Publication History

Abstract

Recently, knowledge-grounded dialogue systems have gained increasing attention. Great efforts have been made to build response matching models where all dialogue content and knowledge sentences are leveraged. However, knowledge redundancy and distraction of irrelevant dialogue content often exist in knowledge-grounded conversations, which may affect the matching process and lead to inferior performance. In addition, irrelevant dialogue history and excessive knowledge also hinder the exploitation of popular pre-trained language models (PLMs) due to the limitation of input length. To address these challenges, we propose a new knowledge-grounded dialogue model based on PLMs, where a knowledge selector and a context selector are designed for filtering out irrelevant knowledge sentences and redundant dialogue history, respectively. Considering the lack of labeled data for the learning of two selectors, we pre-train them with weakly-supervised tasks and then jointly conduct the optimization of knowledge and context selection and fine-tuning of PLMs for response ranking with reinforcement learning (RL). By this means, the dialogue model can distill more accurate and concise knowledge and dialogue content for subsequent response ranking module, and the overall model can converge and perform better. We conduct experiments on two benchmarks and evaluation results indicate that our model can significantly outperform the state-of-the-art methods.

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Index Terms

Learning Multi-turn Response Selection in Grounded Dialogues with Reinforced Knowledge and Context Distillation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Discourse, dialogue and pragmatics
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 4

October 2023

958 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3587261

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 21 April 2023

Online AM: 16 February 2023

Accepted: 06 February 2023

Revised: 04 February 2023

Received: 10 March 2022

Published in TOIS Volume 41, Issue 4

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https://doi.org/10.2478/amns-2024-3569
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