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arXiv · 2109.01924

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

Mingzhi Yu·Diane Litman·Shuang Ma·Jian Wu
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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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Mingzhi Yu, Diane Litman, Shuang Ma, Jian Wu. 2021-09-04. A Neural Network-Based Linguistic Similarity Measure for Entrainment in Conversations. https://arxiv.org/abs/2109.01924

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