2022
DOI: 10.48550/arxiv.2205.11306
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Sample Efficient Approaches for Idiomaticity Detection

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 c… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 8 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?