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

• URL: http://arxiv.org/abs/2109.01924v1
• Date: Sat, 4 Sep 2021 19:48:17 GMT
• Title: A Neural Network-Based Linguistic Similarity Measure for Entrainment in Conversations
• Authors: Mingzhi Yu, Diane Litman, Shuang Ma, Jian Wu
• Abstract summary: Linguistic entrainment is a phenomenon where people tend to mimic each other in conversation. Most of the current similarity measures are based on bag-of-words approaches. We propose to use a neural network model to perform the similarity measure for entrainment.
• Score: 12.052672647509732
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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