Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks

TL;DR

This paper introduces Generative Conversational Networks to automatically generate diverse and engaging conversational data, enhancing training for open-domain social conversational agents, especially with limited seed data.

Contribution

It presents a novel method for data augmentation in conversational AI using generative networks, improving performance with minimal seed data for knowledge-grounded and open-domain conversations.

Findings

GCN produces more engaging and fluent conversations.

Achieves comparable performance with significantly less seed data.

Enhances training efficiency for conversational agents.

Abstract

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.