IITD-DBAI: Multi-Stage Retrieval with Pseudo-Relevance Feedback and Query Reformulation

• URL: http://arxiv.org/abs/2203.17042v1
• Date: Thu, 31 Mar 2022 14:07:47 GMT
• Title: IITD-DBAI: Multi-Stage Retrieval with Pseudo-Relevance Feedback and Query Reformulation
• Authors: Shivani Choudhary
• Abstract summary: Resolving the contextual dependency is one of the most challenging tasks in the Conversational system. Our submission has produced a mean NDCG@3 performance better than the median model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Resolving the contextual dependency is one of the most challenging tasks in the Conversational system. Our submission to CAsT-2021 aimed to preserve the key terms and the context in all subsequent turns and use classical Information retrieval methods. It was aimed to pull as relevant documents as possible from the corpus. We have participated in automatic track and submitted two runs in the CAsT-2021. Our submission has produced a mean NDCG@3 performance better than the median model.

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