declare-lab/CIDER
This repository contains the dataset and the pytorch implementations of the models from the paper CIDER: Commonsense Inference for Dialogue Explanation and Reasoning. CIDER has been accepted to appear at SIGDIAL 2021.
This project helps researchers and developers working with conversational AI understand and explain human dialogues better. It takes raw text dialogues and outputs structured 'knowledge triplets' that show the underlying commonsense connections and reasoning (like cause-and-effect or temporal relations) within the conversation. Anyone building or analyzing conversational systems, such as chatbots or virtual assistants, would use this to dig deeper into how conversations unfold.
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Use this if you are a natural language processing researcher or developer needing to analyze and extract explicit and implicit commonsense knowledge from dialogue data for building more intelligent conversational agents.
Not ideal if you are looking for a ready-to-use application for end-users or a system that directly performs dialogue generation or summarization without an interest in the underlying reasoning.
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Python
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MIT
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Last pushed
Oct 30, 2022
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