Lexical Entrainment for Conversational Systems

TL;DR

This paper introduces a new dataset and measures for lexical entrainment in conversational systems, highlighting its importance for human-like interactions and proposing methods to incorporate LE into response generation models.

Contribution

The work presents a novel dataset, MULTIWOZ-ENTR, new tasks for LE extraction and generation, and baseline approaches to improve LE modeling in conversational agents.

Findings

Current models lack adequate lexical entrainment capabilities.

The proposed dataset enables better evaluation of LE in dialogue systems.

Baseline methods provide initial solutions for LE detection.

Abstract

Conversational agents have become ubiquitous in assisting with daily tasks, and are expected to possess human-like features. One such feature is lexical entrainment (LE), a phenomenon in which speakers in human-human conversations tend to naturally and subconsciously align their lexical choices with those of their interlocutors, leading to more successful and engaging conversations. As an example, if a digital assistant replies 'Your appointment for Jinling Noodle Pub is at 7 pm' to the question 'When is my reservation for Jinling Noodle Bar today?', it may feel as though the assistant is trying to correct the speaker, whereas a response of 'Your reservation for Jinling Noodle Bar is at 7 pm' would likely be perceived as more positive. This highlights the importance of LE in establishing a shared terminology for maximum clarity and reducing ambiguity in conversations. However, we demonstrate in this work that current response generation models do not adequately address this crucial humanlike phenomenon. To address this, we propose a new dataset, named MULTIWOZ-ENTR, and a measure for LE for conversational systems. Additionally, we suggest a way to explicitly integrate LE into conversational systems with two new tasks, a LE extraction task and a LE generation task. We also present two baseline approaches for the LE extraction task, which aim to detect LE expressions from dialogue contexts.