Výsledky vyhledávání - "Zettlemoyer, L."

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Learning Symbolic Models of Stochastic Domains

Autor: Kaelbling, L. P., Pasula, H. M., Zettlemoyer, L. S.

Publikováno v: Journal Of Artificial Intelligence Research, Volume 29, pages 309-352, 2007

In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how suc

Externí odkaz: http://arxiv.org/abs/1110.2211

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Wikipedia Entities as Rendezvous across Languages: Grounding Multilingual Language Models by Predicting Wikipedia Hyperlinks

Autor: Calixto, I., Raganato, A., Pasini, T., Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y.

Publikováno v: The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: NAACL-HLT 2021 : proceedings of the conference : June 6-11, 2021, 3651-3661
STARTPAGE=3651;ENDPAGE=3661;TITLE=The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
NAACL-HLT
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Masked language models have quickly become the de facto standard when processing text. Recently, several approaches have been proposed to further enrich word representations with external knowledge sources such as knowledge graphs. However, these mod

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab4d34426d9da2cc48fcdaafa8e51756
https://dare.uva.nl/personal/pure/en/publications/wikipedia-entities-as-rendezvous-across-languages-grounding-multilingual-language-models-by-predicting-wikipedia-hyperlinks(ff3a2bc3-514f-4b34-91e2-b95e38f2a026).html

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Challenging distributional models with a conceptual network of philosophical terms

Autor: Oortwijn, Y., Bloem, J., Sommerauer, P., Meyer, F., Zhou, W., Fokkens, A., Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y.

Publikováno v: NAACL-HLT
The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: NAACL-HLT 2021 : proceedings of the conference : June 6-11, 2021, 2511-2522
STARTPAGE=2511;ENDPAGE=2522;TITLE=The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Computational linguistic research on language change through distributional semantic (DS) models has inspired researchers from fields such as philosophy and literary studies, who use these methods for the exploration and comparison of comparatively s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb3f94d193d58074e7adb3fe682a08e3
https://doi.org/10.18653/v1/2021.naacl-main.199

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EaSe: A Diagnostic Tool for VQA Based on Answer Diversity

Autor: Jolly, S., Pezzelle, S., Nabi, M., Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y.

Publikováno v: The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: NAACL-HLT 2021 : proceedings of the conference : June 6-11, 2021, 2407-2414
STARTPAGE=2407;ENDPAGE=2414;TITLE=The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
NAACL-HLT

We propose EASE, a simple diagnostic tool for Visual Question Answering (VQA) which quantifies the difficulty of an image, question sample. EASE is based on the pattern of answers provided by multiple annotators to a given question. In particular, it

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74ee792406ed3d7c28443ddd95adc13a
https://doi.org/10.18653/v1/2021.naacl-main.192

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