Say Anything: Automatic Semantic Infelicity Detection in L2 English Indefinite Pronouns

Ella Rabinovich, Julia Watson, Barend Beekhuizen, Suzanne Stevenson

Abstract
Computational research on error detection in second language speakers has mainly addressed clear grammatical anomalies typical to learners at the beginner-to-intermediate level. We focus instead on acquisition of subtle semantic nuances of English indefinite pronouns by non-native speakers at varying levels of proficiency. We first lay out theoretical, linguistically motivated hypotheses, and supporting empirical evidence, on the nature of the challenges posed by indefinite pronouns to English learners. We then suggest and evaluate an automatic approach for detection of atypical usage patterns, demonstrating that deep learning architectures are promising for this task involving nuanced semantic anomalies.
Anthology ID:
K19-1008
Volume:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Mohit Bansal, Aline Villavicencio
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
77–86
Language:
URL:
https://aclanthology.org/K19-1008
DOI:
10.18653/v1/K19-1008
Bibkey:
Cite (ACL):
Ella Rabinovich, Julia Watson, Barend Beekhuizen, and Suzanne Stevenson. 2019. Say Anything: Automatic Semantic Infelicity Detection in L2 English Indefinite Pronouns. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 77–86, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Say Anything: Automatic Semantic Infelicity Detection in L2 English Indefinite Pronouns (Rabinovich et al., CoNLL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl24-info/K19-1008.pdf
Supplementary material:
 K19-1008.Supplementary_Material.zip
Code
 ellarabi/indefinite-pronouns
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