IITD-DBAI: Multi-Stage Retrieval with Pseudo-Relevance Feedback and Query Reformulation
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 8 references that are in the map. First posted on arXiv 31 Mar 2022.
Builds on
3 of 8 references.
References · 3
- 2017Attention Is All You Needcited by 60,361
- 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
- 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 573
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