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How well does active learning.

, и . EMNLP, стр. 296-305. ACL, (2009)

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Semantic clause types and modality as features for argument analysis1., , и . Argument Comput., 8 (2): 95-112 (2017)AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages., , , , , , , , , и 7 other автор(ы). CoRR, (2021)AmericasNLI: Machine translation and natural language inference systems for Indigenous languages of the Americas., , , , , , , , , и 8 other автор(ы). Frontiers Artif. Intell., (2022)UNT Linguistics at SemEval-2020 Task 12: Linear SVC with Pre-trained Word Embeddings as Document Vectors and Targeted Linguistic Features., и . SemEval@COLING, стр. 2209-2215. International Committee for Computational Linguistics, (2020)Genre Distinctions and Discourse Modes: Text Types Differ in their Situation Type Distributions., и . ArgNLP, том 1341 из CEUR Workshop Proceedings, CEUR-WS.org, (2014)Situation entity types: automatic classification of clause-level aspect., , и . ACL (1), The Association for Computer Linguistics, (2016)It's not a Non-Issue: Negation as a Source of Error in Machine Translation., , , и . EMNLP (Findings), том EMNLP 2020 из Findings of ACL, стр. 3869-3885. Association for Computational Linguistics, (2020)How well does active learning., и . EMNLP, стр. 296-305. ACL, (2009)Evaluating FrameNet-style semantic parsing: the role of coverage gaps in FrameNet., и . COLING (Posters), стр. 928-936. Chinese Information Processing Society of China, (2010)Bringing Active Learning to Life., , и . COLING, стр. 949-957. Tsinghua University Press, (2010)