@inproceedings{rabinovich-etal-2019-say,
title = "Say Anything: Automatic Semantic Infelicity Detection in {L}2 {E}nglish Indefinite Pronouns",
author = "Rabinovich, Ella and
Watson, Julia and
Beekhuizen, Barend and
Stevenson, Suzanne",
editor = "Bansal, Mohit and
Villavicencio, Aline",
booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K19-1008",
doi = "10.18653/v1/K19-1008",
pages = "77--86",
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.",
}
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%0 Conference Proceedings
%T Say Anything: Automatic Semantic Infelicity Detection in L2 English Indefinite Pronouns
%A Rabinovich, Ella
%A Watson, Julia
%A Beekhuizen, Barend
%A Stevenson, Suzanne
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F rabinovich-etal-2019-say
%X 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.
%R 10.18653/v1/K19-1008
%U https://aclanthology.org/K19-1008
%U https://doi.org/10.18653/v1/K19-1008
%P 77-86
Markdown (Informal)
[Say Anything: Automatic Semantic Infelicity Detection in L2 English Indefinite Pronouns](https://aclanthology.org/K19-1008) (Rabinovich et al., CoNLL 2019)
ACL