Adapting Sequence Models for Sentence Correction

Allen Schmaltz, Yoon Kim, Alexander Rush, Stuart Shieber


Abstract
In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based models and models that encode subword information via convolutions, and that modeling the output data as a series of diffs improves effectiveness over standard approaches. Our strongest sequence-to-sequence model improves over our strongest phrase-based statistical machine translation model, with access to the same data, by 6 M2 (0.5 GLEU) points. Additionally, in the data environment of the standard CoNLL-2014 setup, we demonstrate that modeling (and tuning against) diffs yields similar or better M2 scores with simpler models and/or significantly less data than previous sequence-to-sequence approaches.
Anthology ID:
D17-1298
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Martha Palmer, Rebecca Hwa, Sebastian Riedel
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2807–2813
Language:
URL:
https://aclanthology.org/D17-1298
DOI:
10.18653/v1/D17-1298
Bibkey:
Cite (ACL):
Allen Schmaltz, Yoon Kim, Alexander Rush, and Stuart Shieber. 2017. Adapting Sequence Models for Sentence Correction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2807–2813, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Adapting Sequence Models for Sentence Correction (Schmaltz et al., EMNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/D17-1298.pdf
Attachment:
 D17-1298.Attachment.zip
Code
 allenschmaltz/grammar
Data
CoNLL-2014 Shared Task: Grammatical Error Correction