activity
20192026
most citedAligned Cross Entropy for Non-Autoregressive Machine Translation

68 citations · 144 across the 8 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.CL2020

Joint Verification and Reranking for Open Fact Checking Over Tables

Michael Schlichtkrull, Vladimir Karpukhin, Barlas Oğuz +3

Structured information is an important knowledge source for automatic verification of factual claims. Nevertheless, the majority of existing research into this task has focused on…

cs.CL2020

UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering

Barlas Oguz, Xilun Chen, Vladimir Karpukhin +6

We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases. Departing from prior…

cs.CL2020

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Patrick Lewis, Ethan Perez, Aleksandra Piktus +9

Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. Howe…

cs.CL2020★ 68 cited

Aligned Cross Entropy for Non-Autoregressive Machine Translation

Marjan Ghazvininejad, Vladimir Karpukhin, Luke Zettlemoyer +1

Non-autoregressive machine translation models significantly speed up decoding by allowing for parallel prediction of the entire target sequence. However, modeling word order is mor…

cs.CL2020

Dense Passage Retrieval for Open-Domain Question Answering

Vladimir Karpukhin, Barlas Oğuz, Sewon Min +5

Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de…