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pro vyhledávání: '"Ostapenko, Alissa"'
Autor:
Song, Yueqi, Cui, Catherine, Khanuja, Simran, Liu, Pengfei, Faisal, Fahim, Ostapenko, Alissa, Winata, Genta Indra, Aji, Alham Fikri, Cahyawijaya, Samuel, Tsvetkov, Yulia, Anastasopoulos, Antonios, Neubig, Graham
Despite the major advances in NLP, significant disparities in NLP system performance across languages still exist. Arguably, these are due to uneven resource allocation and sub-optimal incentives to work on less resourced languages. To track and furt
Externí odkaz:
http://arxiv.org/abs/2305.14716
Natural language processing (NLP) models trained on people-generated data can be unreliable because, without any constraints, they can learn from spurious correlations that are not relevant to the task. We hypothesize that enriching models with speak
Externí odkaz:
http://arxiv.org/abs/2203.08979
Autor:
Arora, Siddhant, Ostapenko, Alissa, Viswanathan, Vijay, Dalmia, Siddharth, Metze, Florian, Watanabe, Shinji, Black, Alan W
Decomposable tasks are complex and comprise of a hierarchy of sub-tasks. Spoken intent prediction, for example, combines automatic speech recognition and natural language understanding. Existing benchmarks, however, typically hold out examples for on
Externí odkaz:
http://arxiv.org/abs/2106.15065
Akademický článek
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Publikováno v:
Machine Translation; Jun2021, Vol. 35 Issue 2, p145-165, 21p
Publikováno v:
Web of Science
Natural language processing (NLP) models trained on people-generated data can be unreliable because, without any constraints, they can learn from spurious correlations that are not relevant to the task. We hypothesize that enriching models with speak
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7905fb59dcf8610115d98bb63c9d3fc9
https://publons.com/wos-op/publon/55337056/
https://publons.com/wos-op/publon/55337056/