Search Results for author: Patrick Lewis

Found 18 papers, 13 papers with code

Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?

no code implementations13 Oct 2021 Xilun Chen, Kushal Lakhotia, Barlas Oğuz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta, Wen-tau Yih

Despite their recent popularity and well known advantages, dense retrievers still lag behind sparse methods such as BM25 in their ability to reliably match salient phrases and rare entities in the query.

Open-Domain Question Answering Passage Retrieval

A Few More Examples May Be Worth Billions of Parameters

1 code implementation8 Oct 2021 Yuval Kirstain, Patrick Lewis, Sebastian Riedel, Omer Levy

We investigate the dynamics of increasing the number of model parameters versus the number of labeled examples across a wide variety of tasks.

Question Answering

Challenges in Generalization in Open Domain Question Answering

no code implementations2 Sep 2021 Linqing Liu, Patrick Lewis, Sebastian Riedel, Pontus Stenetorp

Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions.

Open-Domain Question Answering Systematic Generalization

Domain-matched Pre-training Tasks for Dense Retrieval

1 code implementation28 Jul 2021 Barlas Oğuz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Wen-tau Yih, Sonal Gupta, Yashar Mehdad

Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks.

 Ranked #1 on Passage Retrieval on Natural Questions (using extra training data)

Information Retrieval Passage Retrieval

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

1 code implementation ICLR 2021 Wenhan Xiong, Xiang Lorraine Li, Srini Iyer, Jingfei Du, Patrick Lewis, William Yang Wang, Yashar Mehdad, Wen-tau Yih, Sebastian Riedel, Douwe Kiela, Barlas Oğuz

We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER.

Question Answering

How Context Affects Language Models' Factual Predictions

no code implementations AKBC 2020 Fabio Petroni, Patrick Lewis, Aleksandra Piktus, Tim Rocktäschel, Yuxiang Wu, Alexander H. Miller, Sebastian Riedel

When pre-trained on large unsupervised textual corpora, language models are able to store and retrieve factual knowledge to some extent, making it possible to use them directly for zero-shot cloze-style question answering.

Information Retrieval Language Modelling +2

Dense Passage Retrieval for Open-Domain Question Answering

9 code implementations EMNLP 2020 Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih

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 facto method.

Open-Domain Question Answering Passage Retrieval

Unsupervised Question Decomposition for Question Answering

2 code implementations EMNLP 2020 Ethan Perez, Patrick Lewis, Wen-tau Yih, Kyunghyun Cho, Douwe Kiela

We aim to improve question answering (QA) by decomposing hard questions into simpler sub-questions that existing QA systems are capable of answering.

Question Answering

MLQA: Evaluating Cross-lingual Extractive Question Answering

3 code implementations ACL 2020 Patrick Lewis, Barlas Oğuz, Ruty Rinott, Sebastian Riedel, Holger Schwenk

An alternative to building large monolingual training datasets is to develop cross-lingual systems which can transfer to a target language without requiring training data in that language.

Machine Translation Question Answering

Language Models as Knowledge Bases?

1 code implementation IJCNLP 2019 Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H. Miller, Sebastian Riedel

Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks.

Fine-tuning LAMA +2

Unsupervised Question Answering by Cloze Translation

1 code implementation ACL 2019 Patrick Lewis, Ludovic Denoyer, Sebastian Riedel

We approach this problem by first learning to generate context, question and answer triples in an unsupervised manner, which we then use to synthesize Extractive QA training data automatically.

Question Answering Translation

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