Abstract
Neural language modelling has progressed the state-of-the-art in different downstream Natural Language Processing (NLP) tasks. One such area is of open-domain dialog modelling, neural dialog models based on GPT-2 such as DialoGPT have shown promising performance in single-turn conversation. However, such (neural) dialog models have been criticised for generating responses which although may have relevance to the previous human response, tend to quickly dissipate human interest and descend into trivial conversation. One reason for such performance is the lack of explicit conversation strategy being employed in human-machine conversation. Humans employ a range of conversation strategies while engaging in a conversation, one such key social strategies is Self-disclosure (SD). A phenomenon of revealing information about one-self to others. In this work, Self-disclosure enhancement architecture (SDEA) is introduced utilizing Self-disclosure Topic Model (SDTM) during inference stage of a neural dialog model to re-rank response candidates to enhance self-disclosure in single-turn responses from the model.
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Acknowledgements
This work was conducted with the financial support of the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (D-REAL) under Grant No. 18/CRT/6224. We would like to thank anonymous reviewers from IWSDS 2021 for their valuable comments.
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Soni, M., Cowan, B.R., Wade, V. (2022). Enhancing Self-disclosure In Open-Domain Dialogue By Candidate Re-ranking. In: Stoyanchev, S., Ultes, S., Li, H. (eds) Conversational AI for Natural Human-Centric Interaction. Lecture Notes in Electrical Engineering, vol 943. Springer, Singapore. https://doi.org/10.1007/978-981-19-5538-9_17
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DOI: https://doi.org/10.1007/978-981-19-5538-9_17
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