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Found 5 papers, 0 papers with code
Sign of the Times: Evaluating the use of Large Language Models for Idiomaticity Detection
In particular, how well do such models perform in comparison to encoder-only models fine-tuned specifically for idiomaticity tasks?
Is Less More? Quality, Quantity and Context in Idiom Processing with Natural Language Models
Compositionality in language models presents a problem when processing idiomatic expressions, as their meaning often cannot be directly derived from their individual parts.
Word Boundary Information Isn't Useful for Encoder Language Models
As such, removing these symbols has been shown to have a beneficial effect on the processing of morphologically complex words for transformer encoders in the pretrain-finetune paradigm.
Sample Efficient Approaches for Idiomaticity Detection
In particular we study the impact of Pattern Exploit Training (PET), a few-shot method of classification, and BERTRAM, an efficient method of creating contextual embeddings, on the task of idiomaticity detection.
drsphelps at SemEval-2022 Task 2: Learning idiom representations using BERTRAM
This paper describes our system for SemEval-2022 Task 2 Multilingual Idiomaticity Detection and Sentence Embedding sub-task B.
