Sample Efficient Approaches for Idiomaticity Detection

Dylan Phelps, Xuan-Rui Fan, Edward Gow-Smith, Harish Tayyar Madabushi, Carolina Scarton, Aline Villavicencio

Research output: Chapter in Book/Report/Conference proceedingChapter in a published conference proceeding

Abstract

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 inherently less frequent in natural text. As such, this work explores sample efficient methods of 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. In addition, to further explore generalisability, we focus on the identification of MWEs not present in the training data. Our experiments show that while these methods improve performance on English, they are much less effective on Portuguese and Galician, leading to an overall performance about on par with vanilla mBERT. Regardless, we believe sample efficient methods for both identifying and representing potentially idiomatic MWEs are very encouraging and hold significant potential for future exploration.
Original languageEnglish
Title of host publicationProceedings of the 18th Workshop on Multiword Expressions @LREC2022
Place of PublicationMarseille, France
PublisherEuropean Language Resources Association (ELRA)
Pages105-111
Number of pages7
Publication statusPublished - 1 Jun 2022

Fingerprint

Dive into the research topics of 'Sample Efficient Approaches for Idiomaticity Detection'. Together they form a unique fingerprint.

Cite this