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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)Improving event prediction by representing script participants., and . HLT-NAACL, page 546-551. The Association for Computational Linguistics, (2016)Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification., and . IWCS (1), page 188-199. Association for Computational Linguistics, (2019)Improving Zero-Shot Multilingual Text Generation via Iterative Distillation., , and . COLING, page 5876-5881. International Committee on Computational Linguistics, (2022)Incremental Neo-Davidsonian semantic construction for TAG., and . TAG, page 64-72. ACL, (2012)Barch: an English Dataset of Bar Chart Summaries., , and . LREC, page 3552-3560. European Language Resources Association, (2022)Vector-space calculation of semantic surprisal for predicting word pronunciation duration., , and . ACL (1), page 763-773. The Association for Computer Linguistics, (2015)The semantic augmentation of a psycholinguistically-motivated syntactic formalism., and . CMCL, page 57-65. Association for Computational Linguistics, (2013)Revisiting Sample Size Determination in Natural Language Understanding., , , , , , and . ACL (Findings), page 6716-6724. Association for Computational Linguistics, (2023)