Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models

TL;DR

This paper introduces a multi-task learning approach for neural conversation models that improves speaker-role adaptation by leveraging diverse data, resulting in more personalized and accurate responses without requiring large speaker-specific datasets.

Contribution

It presents a novel multi-task training method that enhances speaker-role modeling in neural conversation systems, reducing data dependency and improving response quality.

Findings

Significant improvements in response quality over baseline models

Better capture of speakers' traits and speaking styles

Model is simple, easy to implement, and data-efficient

Abstract

Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the problem by proposing a multi-task learning approach to training neural conversation models that leverages both conversation data across speakers and other types of data pertaining to the speaker and speaker roles to be modeled. Experiments show that our approach leads to significant improvements over baseline model quality, generating responses that capture more precisely speakers' traits and speaking styles. The model offers the benefits of being algorithmically simple and easy to implement, and not relying on large quantities of data representing specific individual speakers.