Omkar2118/datasets huggingface dataset:
ConvRef

**ConvRef** is a conversational QA benchmark with reformulations. 
It consists of around 11k natural conversations with about 205k reformulations.
ConvRef builds upon the conversational KG-QA benchmark [ConvQuestions](/dataset/convquestions).
Questions come from five different domains: books, movies, music, TV series and soccer and answers are Wikidata entities.
 We used conversation sessions in ConvQuestions as input to our user study. Study participants interacted with a baseline QA system, that was trained using the available paraphrases in ConvQuestions as proxies for reformulations. Users were shown follow-up questions in a given conversation interactively, one after the other, along with the answer coming from the baseline QA system. For wrong answers, the user was prompted to reformulate the question up to four times if needed. In this way, users were able to pose reformulations based on previous wrong answers and the conversation history.

Introducing paper: Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs