Training Conversational Agents with Generative Conversational Networks

• URL: http://arxiv.org/abs/2110.08383v1
• Date: Fri, 15 Oct 2021 21:46:39 GMT
• Title: Training Conversational Agents with Generative Conversational Networks
• Authors: Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim, Dilek Hakkani-Tur
• Abstract summary: We use Generative Conversational Networks to automatically generate data and train social conversational agents. We evaluate our approach on TopicalChat with automatic metrics and human evaluators, showing that with 10% of seed data it performs close to the baseline that uses 100% of the data.
• Score: 74.9941330874663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rich, open-domain textual data available on the web resulted in great advancements for language processing. However, while that data may be suitable for language processing tasks, they are mostly non-conversational, lacking many phenomena that appear in human interactions and this is one of the reasons why we still have many unsolved challenges in conversational AI. In this work, we attempt to address this by using Generative Conversational Networks to automatically generate data and train social conversational agents. We evaluate our approach on TopicalChat with automatic metrics and human evaluators, showing that with 10% of seed data it performs close to the baseline that uses 100% of the data.

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