Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks
This work addresses the challenge of limited conversational data for training AI agents, offering a method to augment data efficiently, though it is incremental as it builds on existing generative network approaches.
The paper tackled the problem of automatically generating conversational data using Generative Conversational Networks to train open-domain social conversational agents, showing that for knowledge-grounded conversations, it achieves performance comparable to using 100% of seed data with only 1% of the data on human ratings of engagingness, fluency, and relevance.
While rich, open-domain textual data are generally available and may include interesting phenomena (humor, sarcasm, empathy, etc.) most are designed for language processing tasks, and are usually in a non-conversational format. In this work, we take a step towards automatically generating conversational data using Generative Conversational Networks, aiming to benefit from the breadth of available language and knowledge data, and train open domain social conversational agents. We evaluate our approach on conversations with and without knowledge on the Topical Chat dataset using automatic metrics and human evaluators. Our results show that for conversations without knowledge grounding, GCN can generalize from the seed data, producing novel conversations that are less relevant but more engaging and for knowledge-grounded conversations, it can produce more knowledge-focused, fluent, and engaging conversations. Specifically, we show that for open-domain conversations with 10\% of seed data, our approach performs close to the baseline that uses 100% of the data, while for knowledge-grounded conversations, it achieves the same using only 1% of the data, on human ratings of engagingness, fluency, and relevance.