Author: Gangi Reddy, R.; Iyer, B.; Sultan, M. A.; Zhang, R.; Sil, A.; Castelli, V.; Florian, R.; Roukos, S.
Title: Synthetic Target Domain Supervision for Open Retrieval QA Cord-id: jxpg1iws Document date: 2021_1_1
ID: jxpg1iws
Snippet: Neural passage retrieval is a new and promising approach in open retrieval question answering. In this work, we stress-test the Dense Passage Retriever (DPR) - -a state-of-the-art (SOTA) open domain neural retrieval model - -on closed and specialized target domains such as COVID-19, and find that it lags behind standard BM25 in this important real-world setting. To make DPR more robust under domain shift, we explore its fine-tuning with synthetic training examples, which we generate from unlabel
Document: Neural passage retrieval is a new and promising approach in open retrieval question answering. In this work, we stress-test the Dense Passage Retriever (DPR) - -a state-of-the-art (SOTA) open domain neural retrieval model - -on closed and specialized target domains such as COVID-19, and find that it lags behind standard BM25 in this important real-world setting. To make DPR more robust under domain shift, we explore its fine-tuning with synthetic training examples, which we generate from unlabeled target domain text using a text-to-text generator. In our experiments, this noisy but fully automated target domain supervision gives DPR a sizable advantage over BM25 in out-of-domain settings, making it a more viable model in practice. Finally, an ensemble of BM25 and our improved DPR model yields the best results, further pushing the SOTA for open retrieval QA on multiple out-of-domain test sets. © 2021 ACM.
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