Say anything: Automatic semantic infelicity detection in L2 English indefinite pronouns

Ella Rabinovich, Julia Watson, Barend Beekhuizen, Suzanne Stevenson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationCoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference
PublisherAssociation for Computational Linguistics
Pages77-86
Number of pages10
ISBN (Electronic)9781950737727
StatePublished - 2019
Externally publishedYes
Event23rd Conference on Computational Natural Language Learning, CoNLL 2019 - Hong Kong, China
Duration: 3 Nov 20194 Nov 2019

Publication series

NameCoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference

Conference

Conference23rd Conference on Computational Natural Language Learning, CoNLL 2019
Country/TerritoryChina
CityHong Kong
Period3/11/194/11/19

Bibliographical note

Publisher Copyright:
© 2019 Association for Computational Linguistics.

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Computational Theory and Mathematics

Fingerprint

Dive into the research topics of 'Say anything: Automatic semantic infelicity detection in L2 English indefinite pronouns'. Together they form a unique fingerprint.

Cite this