Aditya Srikanth Veerubhotla


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R3 : Refined Retriever-Reader pipeline for Multidoc2dial
Srijan Bansal | Suraj Tripathi | Sumit Agarwal | Sireesh Gururaja | Aditya Srikanth Veerubhotla | Ritam Dutt | Teruko Mitamura | Eric Nyberg
Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering

In this paper, we present our submission to the DialDoc shared task based on the MultiDoc2Dial dataset. MultiDoc2Dial is a conversational question answering dataset that grounds dialogues in multiple documents. The task involves grounding a user’s query in a document followed by generating an appropriate response. We propose several improvements over the baseline’s retriever-reader architecture to aid in modeling goal-oriented dialogues grounded in multiple documents. Our proposed approach employs sparse representations for passage retrieval, a passage re-ranker, the fusion-in-decoder architecture for generation, and a curriculum learning training paradigm. Our approach shows a 12 point improvement in BLEU score compared to the baseline RAG model.