A Neural Network-Based Linguistic Similarity Measure for Entrainment in Conversations

TL;DR

This paper introduces a neural network model that measures linguistic similarity in conversations, capturing context and high-level features to better analyze entrainment compared to traditional bag-of-words methods.

Contribution

The paper presents a novel neural network approach that incorporates context-awareness and shared high-level linguistic features for improved conversational entrainment analysis.

Findings

Model outperforms traditional similarity measures

Effective in capturing dialogue context

Promising results in corpus-based analysis

Abstract

Linguistic entrainment is a phenomenon where people tend to mimic each other in conversation. The core instrument to quantify entrainment is a linguistic similarity measure between conversational partners. Most of the current similarity measures are based on bag-of-words approaches that rely on linguistic markers, ignoring the overall language structure and dialogue context. To address this issue, we propose to use a neural network model to perform the similarity measure for entrainment. Our model is context-aware, and it further leverages a novel component to learn the shared high-level linguistic features across dialogues. We first investigate the effectiveness of our novel component. Then we use the model to perform similarity measure in a corpus-based entrainment analysis. We observe promising results for both evaluation tasks.