Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.
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Khandelwal et al. (2020) show that a k-nearest-neighbor (kNN) component improves language modeling performance.
Open Domain Question Answering requires systems to retrieve external knowledge and perform multi-hop reasoning by composing knowledge spread over multiple sentences.
We introduce Talk to Papers, which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search.
Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT.
A sparse representation is known to be an effective means to encode precise lexical cues in information retrieval tasks by associating each dimension with a unique n-gram-based feature.
Open-domain question answering (QA) is known to involve several underlying knowledge and reasoning challenges, but are models actually learning such knowledge when trained on benchmark tasks?