Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Ledell Wu"'
Publikováno v:
Machine Intelligence Research.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d224223e4175e0ac748c91e103787339
https://doi.org/10.1007/s11633-022-1373-9
https://doi.org/10.1007/s11633-022-1373-9
Autor:
Nicola De Cao, Ledell Wu, Kashyap Popat, Mikel Artetxe, Naman Goyal, Mikhail Plekhanov, Luke Zettlemoyer, Nicola Cancedda, Sebastian Riedel, Fabio Petroni
We present mGENRE, a sequence-to-sequence system for the Multilingual Entity Linking (MEL) problem -- the task of resolving language-specific mentions to a multilingual Knowledge Base (KB). For a mention in a given language, mGENRE predicts the name
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4612eccc299ed697a433db2c325d76f
http://arxiv.org/abs/2103.12528
http://arxiv.org/abs/2103.12528
Autor:
Sergey Edunov, Wen-tau Yih, Danqi Chen, Vladimir Karpukhin, Sewon Min, Barlas Oguz, Patrick S. H. Lewis, Ledell Wu
Publikováno v:
EMNLP (1)
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. In this work, we show that retrieval can be practically
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4586e4a9f0e9488de8d1bb2d039be7a3
Publikováno v:
EMNLP (1)
Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we propose a general framework that decomposes gender bias in text along
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1eb19560a5093c357b113a9d7b840cee
Publikováno v:
EMNLP (1)
This paper introduces a conceptually simple, scalable, and highly effective BERT-based entity linking model, along with an extensive evaluation of its accuracy-speed trade-off. We present a two-stage zero-shot linking algorithm, where each entity is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff04dfc77d7995562658e8f20b1714e6
https://doi.org/10.18653/v1/2020.emnlp-main.519
https://doi.org/10.18653/v1/2020.emnlp-main.519
We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification,ranking tasks such as information retrieval/web search,collaborative filtering-based or content-based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ba0b3999daa6552a917f02501913f38