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AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models.

, , , and . EMNLP (Findings), page 3464-3477. Association for Computational Linguistics, (2021)

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Keeping it simple: Implementation and performance of the proto-principle of adaptation and learning in the language sciences., , , and . CoRR, (2020)UoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span Prediction., and . SemEval@ACL/IJCNLP, page 243-248. Association for Computational Linguistics, (2021)UoB-UK at SemEval-2016 Task 1: A Flexible and Extendable System for Semantic Text Similarity using Types, Surprise and Phrase Linking., , and . SemEval@NAACL-HLT, page 680-685. The Association for Computer Linguistics, (2016)Faster BERT-based Re-ranking through Candidate Passage Extraction., and . TREC, volume 1266 of NIST Special Publication, National Institute of Standards and Technology (NIST), (2020)UoB at SemEval-2020 Task 12: Boosting BERT with Corpus Level Information., and . SemEval@COLING, page 2216-2221. International Committee for Computational Linguistics, (2020)CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets - RoBERTa Ensembles and The Continued Relevance of Handcrafted Features., and . W-NUT@EMNLP, page 352-358. Association for Computational Linguistics, (2020)Extended Overview of the CLEF-2023 LongEval Lab on Longitudinal Evaluation of Model Performance., , , , , , , , , and 9 other author(s). CLEF (Working Notes), volume 3497 of CEUR Workshop Proceedings, page 2181-2203. CEUR-WS.org, (2023)CxGBERT: BERT meets Construction Grammar., , , and . COLING, page 4020-4032. International Committee on Computational Linguistics, (2020)UoB at AI-SOCO 2020: Approaches to Source Code Classification and the Surprising Power of n-grams., and . FIRE (Working Notes), volume 2826 of CEUR Workshop Proceedings, page 677-693. CEUR-WS.org, (2020)CogNLP-Sheffield at CMCL 2021 Shared Task: Blending Cognitively Inspired Features with Transformer-based Language Models for Predicting Eye Tracking Patterns., , , and . CMLS, page 125-133. Association for Computational Linguistics, (2021)