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Despite the recent ubiquity of large language models and their high zero-shot prompted performance across a wide range of tasks, it is still not known how well they perform on tasks which require processing of potentially idiomatic language. In parti
Externí odkaz:
http://arxiv.org/abs/2405.09279
Autor:
Knietaite, Agne, Allsebrook, Adam, Minkov, Anton, Tomaszewski, Adam, Slinko, Norbert, Johnson, Richard, Pickard, Thomas, Phelps, Dylan, Villavicencio, Aline
Compositionality in language models presents a problem when processing idiomatic expressions, as their meaning often cannot be directly derived from their individual parts. Although fine-tuning and other optimization strategies can be used to improve
Externí odkaz:
http://arxiv.org/abs/2405.08497
Autor:
Phelps, Dylan, Fan, Xuan-Rui, Gow-Smith, Edward, Madabushi, Harish Tayyar, Scarton, Carolina, Villavicencio, Aline
Deep neural models, in particular Transformer-based pre-trained language models, require a significant amount of data to train. This need for data tends to lead to problems when dealing with idiomatic multiword expressions (MWEs), which are inherentl
Externí odkaz:
http://arxiv.org/abs/2205.11306
Autor:
Phelps, Dylan
This paper describes our system for SemEval-2022 Task 2 Multilingual Idiomaticity Detection and Sentence Embedding sub-task B. We modify a standard BERT sentence transformer by adding embeddings for each idioms, which are created using BERTRAM and a
Externí odkaz:
http://arxiv.org/abs/2204.02821