NORMY: Non-Uniform History Modeling for Open Retrieval Conversational Question Answering

Understand

Open Retrieval Conversational Question Answering (OrConvQA) answers a question given a conversation as context and a document collection.

• A typical OrConvQA pipeline consists of three modules: a Retriever to retrieve relevant documents from the collection, a Reranker to rerank them given the question and the context, and a Reader to extract an answer span.
• The conversational turns can provide valuable context to answer the final query.
• State-of-the-art OrConvQA systems use the same history modeling for all three modules of the pipeline.