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Discourse expectations raised by contrastive connectives

, , , and . Conference on Discourse Expectations: Theoretical, Experimental, and Computational Perspectives (DETEC), (2015)

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Examples and Specifications that Prove a Point: Identifying Elaborative and Argumentative Discourse Relations., and . Dialogue Discourse, 8 (2): 56-83 (2017)Syntactic complexity induces explicit grounding in the Maptask corpus., , and . INTERSPEECH, page 542. ISCA, (2008)Improving fit to human reading times via temperature-scaled surprisal., , and . CoRR, (2023)Unsupervised Pidgin Text Generation By Pivoting English Data and Self-Training., , , and . AfricaNLP, (2020)Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification., and . IWCS (1), page 188-199. Association for Computational Linguistics, (2019)Improving event prediction by representing script participants., and . HLT-NAACL, page 546-551. The Association for Computational Linguistics, (2016)Psycholinguistic Models of Sentence Processing Improve Sentence Readability Ranking., and . EACL (1), page 958-968. Association for Computational Linguistics, (2017)Improving Zero-Shot Multilingual Text Generation via Iterative Distillation., , and . COLING, page 5876-5881. International Committee on Computational Linguistics, (2022)Barch: an English Dataset of Bar Chart Summaries., , and . LREC, page 3552-3560. European Language Resources Association, (2022)The semantic augmentation of a psycholinguistically-motivated syntactic formalism., and . CMCL, page 57-65. Association for Computational Linguistics, (2013)