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All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality.

, and . EMNLP (1), page 4527-4546. Association for Computational Linguistics, (2021)

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Hierarchic syntax improves reading time prediction., and . HLT-NAACL, page 1597-1605. The Association for Computational Linguistics, (2015)Discourse Context Predictability Effects in Hindi Word Order., , , and . EMNLP, page 10390-10406. Association for Computational Linguistics, (2022)Quantity doesn't buy quality syntax with neural language models., , and . EMNLP/IJCNLP (1), page 5830-5836. Association for Computational Linguistics, (2019)An Analysis of Memory-based Processing Costs using Incremental Deep Syntactic Dependency Parsing., , and . CMCL, page 37-46. Association for Computational Linguistics, (2013)Evidence of syntactic working memory usage in MEG data., , and . CMCL@NAACL-HLT, page 79-88. Association for Computational Linguistics, (2015)Bootstrapping into Filler-Gap: An Acquisition Story., and . ACL (1), page 1084-1093. The Association for Computer Linguistics, (2014)Memory access during incremental sentence processing causes reading time latency., , , , and . CL4LC@COLING 2016, page 49-58. The COLING 2016 Organizing Committee, (2016)Connectionist-Inspired Incremental PCFG Parsing., , and . CMCL@NAACL-HLT, page 51-60. Association for Computational Linguistics, (2012)Linguistic Compression in Single-Sentence Human-Written Summaries., and . EMNLP (Findings), page 7922-7935. Association for Computational Linguistics, (2023)Dual Mechanism Priming Effects in Hindi Word Order., , , and . AACL/IJCNLP (1), page 936-953. Association for Computational Linguistics, (2022)