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Neural Data-to-Text Generation with LM-based Text Augmentation.

, , , , and . EACL, page 758-768. Association for Computational Linguistics, (2021)

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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)Unsupervised Pidgin Text Generation By Pivoting English Data and Self-Training., , , and . AfricaNLP, (2020)Improving fit to human reading times via temperature-scaled surprisal., , and . CoRR, (2023)Improving event prediction by representing script participants., and . HLT-NAACL, page 546-551. The Association for Computational Linguistics, (2016)Improving Zero-Shot Multilingual Text Generation via Iterative Distillation., , and . COLING, page 5876-5881. International Committee on Computational Linguistics, (2022)A bathtub by any other name: the reduction of German compounds in predictive contexts., and . CogSci, cognitivesciencesociety.org, (2021)Partner effects and individual differences on perspective taking., and . CogSci, cognitivesciencesociety.org, (2022)Incremental Neo-Davidsonian semantic construction for TAG., and . TAG, page 64-72. ACL, (2012)Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification., and . IWCS (1), page 188-199. Association for Computational Linguistics, (2019)Psycholinguistic Models of Sentence Processing Improve Sentence Readability Ranking., and . EACL (1), page 958-968. Association for Computational Linguistics, (2017)