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Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering
2021
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
unpublished
Commonly used information retrieval methods such as TF-IDF in open-domain question answering (QA) systems are insufficient to capture deep semantic matching that goes beyond lexical overlaps. Some recent studies consider the retrieval process as maximum inner product search (MIPS) using dense question and paragraph representations, achieving promising results on several informationseeking QA datasets. However, the pretraining of the dense vector representations is highly resource-demanding,
doi:10.18653/v1/2021.eacl-main.244
fatcat:c7r6jcp4xrgmfbwm47xmbjwuoi