Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning

TL;DR

This paper introduces a role-based contextual model that captures speaker-specific behaviors in multi-turn dialogues, significantly enhancing language understanding and dialogue policy learning in conversational systems.

Contribution

It presents a novel role-based modeling approach that independently considers speaker roles, improving the effectiveness of dialogue understanding and policy learning.

Findings

Role-specific behavioral patterns are effectively learned.

Significant improvements in language understanding accuracy.

Enhanced dialogue policy performance.

Abstract

Language understanding (LU) and dialogue policy learning are two essential components in conversational systems. Human-human dialogues are not well-controlled and often random and unpredictable due to their own goals and speaking habits. This paper proposes a role-based contextual model to consider different speaker roles independently based on the various speaking patterns in the multi-turn dialogues. The experiments on the benchmark dataset show that the proposed role-based model successfully learns role-specific behavioral patterns for contextual encoding and then significantly improves language understanding and dialogue policy learning tasks.