Search Results for author: Vladimir Karpukhin

Found 13 papers, 7 papers with code

CM3: A Causal Masked Multimodal Model of the Internet

no code implementations19 Jan 2022 Armen Aghajanyan, Bernie Huang, Candace Ross, Vladimir Karpukhin, Hu Xu, Naman Goyal, Dmytro Okhonko, Mandar Joshi, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer

We introduce CM3, a family of causally masked generative models trained over a large corpus of structured multi-modal documents that can contain both text and image tokens.

Entity Disambiguation Entity Linking

The Web Is Your Oyster -- Knowledge-Intensive NLP against a Very Large Web Corpus

2 code implementations18 Dec 2021 Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Dmytro Okhonko, Samuel Broscheit, Gautier Izacard, Patrick Lewis, Barlas Oğuz, Edouard Grave, Wen-tau Yih, Sebastian Riedel

In order to address increasing demands of real-world applications, the research for knowledge-intensive NLP (KI-NLP) should advance by capturing the challenges of a truly open-domain environment: web-scale knowledge, lack of structure, inconsistent quality and noise.

Common Sense Reasoning Retrieval

Discourse-Aware Soft Prompting for Text Generation

no code implementations10 Dec 2021 Marjan Ghazvininejad, Vladimir Karpukhin, Vera Gor, Asli Celikyilmaz

We show that soft-prompt based conditional text generation can be improved with simple and efficient methods that simulate modeling the discourse structure of human written text.

Blocking Conditional Text Generation +1

Dense Passage Retrieval for Open-Domain Question Answering

17 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 +1

Aligned Cross Entropy for Non-Autoregressive Machine Translation

1 code implementation ICML 2020 Marjan Ghazvininejad, Vladimir Karpukhin, Luke Zettlemoyer, Omer Levy

This difficultly is compounded during training with cross entropy loss, which can highly penalize small shifts in word order.

Machine Translation Translation

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