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Title: Unified Representation for Non-compositional and Compositional Expressions
Accurate processing of non-compositional language relies on generating good representations for such expressions. In this work, we study the representation of language non-compositionality by proposing a language model, PIER+, that builds on BART and can create semantically meaningful and contextually appropriate representations for English potentially idiomatic expressions (PIEs). PIEs are characterized by their non-compositionality and contextual ambiguity in their literal and idiomatic interpretations. Via intrinsic evaluation on embedding quality and extrinsic evaluation on PIE processing and NLU tasks, we show that representations generated by PIER+ result in 33% higher homogeneity score for embedding clustering than BART, whereas 3.12% and 3.29% gains in accuracy and sequence accuracy for PIE sense classification and span detection compared to the state-of-the-art IE representation model, GIEA. These gains are achieved without sacrificing PIER+’s performance on NLU tasks (+/- 1% accuracy) compared to BART.  more » « less
Award ID(s):
2229612 2230817
PAR ID:
10496730
Author(s) / Creator(s):
;
Publisher / Repository:
Association for Computational Linguistics
Date Published:
Journal Name:
Findings of the Association for Computational Linguistics: EMNLP 2023
Page Range / eLocation ID:
11696 to 11710
Format(s):
Medium: X
Location:
Singapore
Sponsoring Org:
National Science Foundation
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