Automatic Extraction of Learner Errors in ESL Sentences Using Linguistically Enhanced Alignments

Mariano Felice, Christopher Bryant, Ted Briscoe


Abstract
We propose a new method of automatically extracting learner errors from parallel English as a Second Language (ESL) sentences in an effort to regularise annotation formats and reduce inconsistencies. Specifically, given an original and corrected sentence, our method first uses a linguistically enhanced alignment algorithm to determine the most likely mappings between tokens, and secondly employs a rule-based function to decide which alignments should be merged. Our method beats all previous approaches on the tested datasets, achieving state-of-the-art results for automatic error extraction.
Anthology ID:
C16-1079
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
825–835
Language:
URL:
https://aclanthology.org/C16-1079
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/C16-1079.pdf
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