golsun/SpaceFusion
NAACL'19: "Jointly Optimizing Diversity and Relevance in Neural Response Generation"
This project helps conversational AI developers create more engaging chatbots and virtual assistants. It takes existing dialogue datasets and conversation models as input, then produces a refined model that generates chat responses that are both relevant to the conversation and diverse in their phrasing. This is ideal for AI researchers and engineers working on natural language generation for dialogue systems.
No commits in the last 6 months.
Use this if you are building conversational AI and need to improve the quality of generated responses by making them less repetitive and more pertinent.
Not ideal if you are looking for a pre-trained, production-ready chatbot or a tool for general natural language processing tasks outside of dialogue generation.
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Language
Python
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Last pushed
Sep 28, 2020
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