What Meaning-Form Correlation Has to Compose With: A Study of MFC on Artificial and Natural Language

Timothee Mickus, Timothée Bernard, Denis Paperno


Abstract
Compositionality is a widely discussed property of natural languages, although its exact definition has been elusive. We focus on the proposal that compositionality can be assessed by measuring meaning-form correlation. We analyze meaning-form correlation on three sets of languages: (i) artificial toy languages tailored to be compositional, (ii) a set of English dictionary definitions, and (iii) a set of English sentences drawn from literature. We find that linguistic phenomena such as synonymy and ungrounded stop-words weigh on MFC measurements, and that straightforward methods to mitigate their effects have widely varying results depending on the dataset they are applied to. Data and code are made publicly available.
Anthology ID:
2020.coling-main.333
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3737–3749
Language:
URL:
https://aclanthology.org/2020.coling-main.333
DOI:
10.18653/v1/2020.coling-main.333
Bibkey:
Cite (ACL):
Timothee Mickus, Timothée Bernard, and Denis Paperno. 2020. What Meaning-Form Correlation Has to Compose With: A Study of MFC on Artificial and Natural Language. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3737–3749, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
What Meaning-Form Correlation Has to Compose With: A Study of MFC on Artificial and Natural Language (Mickus et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.333.pdf
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