Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Amit Gajbhiye"'
Autor:
Fernando Alva-Manchego, Abiola Obamuyide, Amit Gajbhiye, Frédéric Blain, Marina Fomicheva, Lucia Specia
Publikováno v:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation (QE). deepQuest-py provides access to (1) state-of-the-art models based on pre-trained Transformers for sentence-level and word
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a1a4d9cdf219e42d2832440618010ac6
https://orca.cardiff.ac.uk/id/eprint/147257/1/2021.emnlp-demo.42.pdf
https://orca.cardiff.ac.uk/id/eprint/147257/1/2021.emnlp-demo.42.pdf
Autor:
Amit Gajbhiye, Frédéric Blain, Nikolaos Aletras, Marina Fomicheva, Abiola Obamuyide, Lucia Specia, Fernando Alva-Manchego
Publikováno v:
ACL/IJCNLP (Findings)
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Quality Estimation (QE) is the task of automatically predicting Machine Translation quality in the absence of reference translations, making it applicable in real-time settings, such as translating online social media conversations. Recent success in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a4d0a97c9009067baf09928b631375c
Publikováno v:
Farkaš, Igor & Masulli, Paolo & Wermter, Stefan (Eds.). Artificial Neural Networks and Machine Learning – ICANN 2020. : Springer, pp. 633-646, Lecture notes in computer science, Vol.12396
Artificial Neural Networks and Machine Learning – ICANN 2020 ISBN: 9783030616083
ICANN (1)
Artificial Neural Networks and Machine Learning – ICANN 2020 ISBN: 9783030616083
ICANN (1)
We consider the task of incorporating real-world commonsense knowledge into deep Natural Language Inference (NLI) models. Existing external knowledge incorporation methods are limited to lexical-level knowledge and lack generalization across NLI mode
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1322bc143eddfeb8fb1d426299d98fab
https://doi.org/10.1007/978-3-030-61609-0_50
https://doi.org/10.1007/978-3-030-61609-0_50
Publikováno v:
Abe, Naoki & Liu, Huan & Pu, Calton & Hu, Xiaohua & Ahmed, Nesreen & Qiao, Mu & Song, Yang & Kossmann, Donald & Liu, Bing & Lee, Kisung & Tang, Jiliang & He, Jingrui & Saltz, Jeffrey (Eds.). (2018). 2018 IEEE International Conference on Big Data (Big Data) ; proceedings. Piscataway, N.J.: IEEE, pp. 1009-1014
IEEE BigData
IEEE BigData
Natural Language Inference (NLI) is a fundamental step towards natural language understanding. The task aims to detect whether a premise entails or contradicts a given hypothesis. NLI contributes to a wide range of natural language understanding appl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c6065f1df5751b0f46d1fb9e809ef46
http://dro.dur.ac.uk/26801/
http://dro.dur.ac.uk/26801/
Publikováno v:
Artificial Neural Networks and Machine Learning – ICANN 2018 ISBN: 9783030014230
Kurková, V. & Manolopoulos, Yannis & Hammer, Barbara & Iliadis, Lazaros S. & Maglogiannis, Ilias G. (Eds.). (2018). Artificial neural networks and machine learning-ICANN 2018 : 27th international Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, proceedings. Part III. Cham: Springer, pp. 157-167, Lecture notes in computer science(11141)
Kurková, V. & Manolopoulos, Yannis & Hammer, Barbara & Iliadis, Lazaros S. & Maglogiannis, Ilias G. (Eds.). (2018). Artificial neural networks and machine learning-ICANN 2018 : 27th international Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, proceedings. Part III. Cham: Springer, pp. 157-167, Lecture notes in computer science(11141)
Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In thi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6dff255be61dd2c29f0b373bc256f455
https://doi.org/10.1007/978-3-030-01424-7_16
https://doi.org/10.1007/978-3-030-01424-7_16
Publikováno v:
2014 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e367febbfc4a664b07264fe5aefaf023
https://doi.org/10.1109/confluence.2014.6949224
https://doi.org/10.1109/confluence.2014.6949224
