Year: 2017
Type: conference-paper
Abstract: In a controlled experiment of sequence-tosequence approaches for the task of sentence correction, we find that characterbased 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 M 2 (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 M 2 scores with simpler models and/or significantly less data than previous sequence-to-sequence approaches.
DOI: 10.18653/v1/d17-1298
Institutions: Harvard University, Meta (Israel)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2741093773