Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?

Ali Hakimi Parizi, Paul Cook


Abstract
In this paper, we propose the first model for multiword expression (MWE) compositionality prediction based on character-level neural network language models. Experimental results on two kinds of MWEs (noun compounds and verb-particle constructions) and two languages (English and German) suggest that character-level neural network language models capture knowledge of multiword expression compositionality, in particular for English noun compounds and the particle component of English verb-particle constructions. In contrast to many other approaches to MWE compositionality prediction, this character-level approach does not require token-level identification of MWEs in a training corpus, and can potentially predict the compositionality of out-of-vocabulary MWEs.
Anthology ID:
W18-4920
Volume:
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
LAW
SIGs:
SIGLEX | SIGANN
Publisher:
Association for Computational Linguistics
Note:
Pages:
185–192
Language:
URL:
https://aclanthology.org/W18-4920
DOI:
Bibkey:
Cite (ACL):
Ali Hakimi Parizi and Paul Cook. 2018. Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?. In Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018), pages 185–192, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality? (Hakimi Parizi & Cook, LAW 2018)
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PDF:
https://aclanthology.org/W18-4920.pdf