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Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs.

, , , и . EMNLP/IJCNLP (1), стр. 4184-4194. Association for Computational Linguistics, (2019)

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Comparing Word Representations for Implicit Discourse Relation Classification., и . EMNLP, стр. 2201-2211. The Association for Computational Linguistics, (2015)Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs., , , и . EMNLP/IJCNLP (1), стр. 4184-4194. Association for Computational Linguistics, (2019)Is writing style predictive of scientific fraud?, и . CoRR, (2017)Investigation par méthodes d'apprentissage des spécificités langagières propres aux personnes avec schizophrénie (Investigating Learning Methods Applied to Language Specificity of Persons with Schizophrenia)., , , , , и . JEP-TALN-RECITAL (2), стр. 12-26. ATALA et AFCP, (2020)Which aspects of discourse relations are hard to learn? Primitive decomposition for discourse relation classification., , и . SIGdial, стр. 432-441. Association for Computational Linguistics, (2019)Combining Natural and Artificial Examples to Improve Implicit Discourse Relation Identification., и . COLING, стр. 1694-1705. ACL, (2014)Multi-view and multi-task training of RST discourse parsers., , и . COLING, стр. 1903-1913. ACL, (2016)Zero-shot Learning for Multilingual Discourse Relation Classification., , , и . LREC/COLING, стр. 17858-17876. ELRA and ICCL, (2024)MELODI at SemEval-2023 Task 3: In-domain Pre-training for Low-resource Classification of News Articles., , и . SemEval@ACL, стр. 108-113. Association for Computational Linguistics, (2023)Learning Connective-based Word Representations for Implicit Discourse Relation Identification., и . EMNLP, стр. 203-213. The Association for Computational Linguistics, (2016)