Training Conversational Agents with Generative Conversational Networks
This addresses data scarcity in conversational AI, though it is incremental as it builds on existing methods for data generation.
The paper tackled the problem of insufficient conversational data for training AI agents by using Generative Conversational Networks to automatically generate data, achieving performance close to a baseline using only 10% of seed data on the TopicalChat dataset.
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.