A Neural Network-Based Linguistic Similarity Measure for Entrainment in Conversations
This work addresses the need for more accurate similarity measures in conversational analysis, though it appears incremental by building on existing neural methods.
The authors tackled the problem of quantifying linguistic entrainment in conversations by proposing a neural network-based similarity measure that incorporates dialogue context and shared high-level features, observing promising results in corpus-based analysis.
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.