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Enhancing Self-Disclosure In Neural Dialog Models By Candidate Re-ranking by Soni, Mayank; Cowan, Benjamin; Wade, Vincent is a scholarly article available to read on EtoBox.

What is Enhancing Self-Disclosure In Neural Dialog Models By Candidate Re-ranking about?

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 criticized 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. Social penetration theory (SPT) proposes that communication between two people moves from shallow to deeper levels as the relationship progresses primarily through self-disclosure. Disclosure helps in creating rapport among the participants engaged in a conversation. In this paper, Self-disclosure enhancement architecture (SDEA) is introduced utilizin

Author

Soni, Mayank; Cowan, Benjamin; Wade, Vincent

Published

2021

Language

EN