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A response generator with response-aware encoder for generating specific and relevant responses

  • Data analytics and machine learning
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Abstract

The dialogue data usually consist of the pairs of a query and its response, but no previous response generators have exploited the responses explicitly in their training while a response provides significant information about the meaning of a query. Therefore, this paper proposes a sequence-to-sequence response generator with a response-aware encoder. The proposed generator exploits golden responses by reflecting them into query representation. For this purpose, the response-aware encoder adds a relevancy scorer layer to the transformer encoder that calculates the relevancy of query tokens to a response. However, golden responses are available only during training of the response generator and unavailable at inference time. As a solution to this problem, the joint learning of a teacher and a student relevancy scorer is adopted. That is, at the training time, both the teacher and the student relevancy scorers are optimized but the decoder generates a response using only the relevancy of the teacher scorer. However, at the inference time, the decoder uses that of the student scorer. Since the student scorer is trained to minimize the difference from the teacher scorer, it can be used to compute the relevancy of a prospective response. The proposed model is the first attempt to use a golden response directly for generating a query representation, whereas previous studies used the responses for its implicit and indirect reflection. As a result, it achieved higher dialogue evaluation score than the current state-of-the-art model for Reddit, Persona-Chat, and DailyDialog data sets.

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Data availability and materials

The datasets generated during and/or analysed during the current study are available in the ACL2020-ConKADI repository, https://github.com/pku-sixing/ACL2020-ConKADI.

Notes

  1. https://github.com/microsoft/MASS/tree/master/MASS-summarization.

  2. https://github.com/pytorch/fairseq/blob/master/examples/bart.

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Funding

This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korea government (MSIT) (No. 2013-0-00109, WiseKB: Big data based self-evolving knowledge base and reasoning platform) and the Korea Government (MSIT) (Artificial Intelligence Innovation Hub) under Grant 2021-0-0206 and the National Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (No. 2020R1A4A1018607)

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Kyung Hee University, 1732 Deogyeong, Yongin, Gyeonggi, 17104, Republic of Korea

    So-Eon Kim & Seong-Bae Park

  2. Department of Information Technology, Jeonbuk National University, 567, Baekje, Jeonju, Jeollabuk, 54896, Republic of Korea

    Hyun-Je Song

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  1. So-Eon Kim
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection were performed by So-Eon Kim and data analysis was performed by Hyun-Je Song. The first draft of the manuscript was written by So-Eon Kim and reviewing and editing the manuscript were performed by Seong-Bae Park. All authors read and approved the final manuscript.

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Correspondence to Seong-Bae Park.

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Kim, SE., Song, HJ. & Park, SB. A response generator with response-aware encoder for generating specific and relevant responses. Soft Comput 27, 3721–3732 (2023). https://doi.org/10.1007/s00500-022-07664-x

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