NORMY: Non-Uniform History Modeling for Open Retrieval Conversational Question Answering
<|reference_start|>NORMY: Non-Uniform History Modeling for Open Retrieval Conversational Question Answering: 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. We hypothesize this as suboptimal. Specifically, we argue that a broader context is needed in the first modules of the pipeline to not miss relevant documents, while a narrower context is needed in the last modules to identify the exact answer span. We propose NORMY, the first unsupervised non-uniform history modeling pipeline which generates the best conversational history for each module. We further propose a novel Retriever for NORMY, which employs keyphrase extraction on the conversation history, and leverages passages retrieved in previous turns as additional context. We also created a new dataset for OrConvQA, by expanding the doc2dial dataset. We implemented various state-of-the-art history modeling techniques and comprehensively evaluated them separately for each module of the pipeline on three datasets: OR-QUAC, our doc2dial extension, and ConvMix. Our extensive experiments show that NORMY outperforms the state-of-the-art in the individual modules and in the end-to-end system.<|reference_end|>
@article{rashid2024normy:,
    title={NORMY: Non-Uniform History Modeling for Open Retrieval Conversational
  Question Answering},
    author={Muhammad Shihab Rashid, Jannat Ara Meem, Vagelis Hristidis},
    journal={arXiv preprint arXiv:2402.04548},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04548},
    primaryClass={cs.IR}
}