Zobrazeno 1 - 10
of 241
pro vyhledávání: '"Ekaterina Shutova"'
Publikováno v:
Computational Linguistics, Vol 49, Iss 3 (2023)
Externí odkaz:
https://doaj.org/article/8166aa93944c4c7fa40a432377694af9
Autor:
Ekaterina Shutova
Publikováno v:
Computational Linguistics, Vol 41, Iss 4 (2023)
Externí odkaz:
https://doaj.org/article/40bb119be8d146bab6c891a7916d13ae
Publikováno v:
Computational Linguistics, Vol 39, Iss 2 (2023)
Externí odkaz:
https://doaj.org/article/7787016b02b8471bbb1f732bcae4e4bf
Autor:
Rochelle Choenni, Ekaterina Shutova
Publikováno v:
Computational Linguistics, Vol 48, Iss 3 (2022)
Multilingual sentence encoders have seen much success in cross-lingual model transfer for downstream NLP tasks. The success of this transfer is, however, dependent on the model’s ability to encode the patterns of cross-lingual similarity and variat
Externí odkaz:
https://doaj.org/article/2b8cb6e381104f02940f817654711dae
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 4 (2021)
Externí odkaz:
https://doaj.org/article/7f1e8904204040e4ac9d8df5c7e8836b
Publikováno v:
Computational Linguistics, Vol 43, Iss 1 (2016)
Highly frequent in language and communication, metaphor represents a significant challenge for Natural Language Processing (NLP) applications. Computational work on metaphor has traditionally evolved around the use of hand-coded knowledge, making the
Externí odkaz:
https://doaj.org/article/f851d1ded5cd47c69348a61e12ed5bcd
Publikováno v:
AAAI
AAAI-20, IAAI-20, EAAI-20 proceedings: Thirty-Fourth AAAI Conference on Artificial Intelligence, Thirty-Second Conference on Innovative Applications of Artificial Intelligence, The Tenth Symposium on Educational Advances in Artificial Intelligence : February 7–12th, 2020, New York Hilton Midtown, New York, New York, USA, 5, 9090-9097
AAAI-20, IAAI-20, EAAI-20 proceedings: Thirty-Fourth AAAI Conference on Artificial Intelligence, Thirty-Second Conference on Innovative Applications of Artificial Intelligence, The Tenth Symposium on Educational Advances in Artificial Intelligence : February 7–12th, 2020, New York Hilton Midtown, New York, New York, USA, 5, 9090-9097
The lack of annotated data in many languages is a well-known challenge within the field of multilingual natural language processing (NLP). Therefore, many recent studies focus on zero-shot transfer learning and joint training across languages to over
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::940017f1fbbdc161c9dde825480a9639
https://doi.org/10.1609/aaai.v34i05.6443
https://doi.org/10.1609/aaai.v34i05.6443
Publikováno v:
Transactions of the Association of Computational Linguistics, 8, 231-246. MIT Press Journals
Transactions of the Association for Computational Linguistics, Vol 8, Pp 231-246 (2020)
Transactions of the Association for Computational Linguistics, Vol 8, Pp 231-246 (2020)
Recent years have seen a growing interest within the natural language processing (NLP) community in evaluating the ability of semantic models to capture human meaning representation in the brain. Existing research has mainly focused on applying seman
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28ae1ed1000ed74af8eb027667183ec7
https://doi.org/10.1162/tacl_a_00307
https://doi.org/10.1162/tacl_a_00307
Publikováno v:
CHI Extended Abstracts
Metaphorical thinking acts as a bridge between embodiment and abstraction and helps to flexibly organize human knowledge and behavior. Yet its role in embodied human-computer interface de- sign, and its potential for supporting goals such as self-awa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::17eaf8a7b5c392589904a263e1befa04
https://research.gold.ac.uk/id/eprint/29800/1/DjokicShutovaFiebrink_CHI2021Interactivity.pdf
https://research.gold.ac.uk/id/eprint/29800/1/DjokicShutovaFiebrink_CHI2021Interactivity.pdf
Autor:
Anna Langedijk, Verna Dankers, Phillip Lippe, Sander Bos, Bryan Cardenas Guevara, Helen Yannakoudakis, Ekaterina Shutova
Meta-learning, or learning to learn, is a technique that can help to overcome resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to new tasks. We apply model-agnostic meta-learning (MAML) to the task of cross-lingual depende
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6185a079390dfaae0b607c5625306563
http://arxiv.org/abs/2104.04736
http://arxiv.org/abs/2104.04736