LCS2-IIITD/SPARTA_WSDM2022

This repository contains the code and dataset for our paper titled Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations accepted at WSDM Conference, 2022.

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This project helps researchers and practitioners analyze counselling conversations to identify different types of speaker intents. By inputting conversation transcripts, it can classify dialogue acts like questions, affirmations, or advice, providing insights into the dynamics of therapeutic interactions. It is designed for researchers studying human communication, particularly in therapeutic or support contexts.

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Use this if you are a researcher or NLP specialist aiming to automatically categorize dialogue acts within counselling or similar conversational data.

Not ideal if you need an out-of-the-box solution for real-time therapeutic support or for analyzing highly domain-specific conversations outside of counselling.

counselling-research dialogue-analysis natural-language-processing conversation-transcription speaker-intent-classification
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Python

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Jun 24, 2022

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