29 research outputs found
Adapting Sequence Models for Sentence Correction
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.Comment: EMNLP 201
A Nested Attention Neural Hybrid Model for Grammatical Error Correction
Grammatical error correction (GEC) systems strive to correct both global
errors in word order and usage, and local errors in spelling and inflection.
Further developing upon recent work on neural machine translation, we propose a
new hybrid neural model with nested attention layers for GEC. Experiments show
that the new model can effectively correct errors of both types by
incorporating word and character-level information,and that the model
significantly outperforms previous neural models for GEC as measured on the
standard CoNLL-14 benchmark dataset. Further analysis also shows that the
superiority of the proposed model can be largely attributed to the use of the
nested attention mechanism, which has proven particularly effective in
correcting local errors that involve small edits in orthography
Referenceless Quality Estimation for Natural Language Generation
Traditional automatic evaluation measures for natural language generation
(NLG) use costly human-authored references to estimate the quality of a system
output. In this paper, we propose a referenceless quality estimation (QE)
approach based on recurrent neural networks, which predicts a quality score for
a NLG system output by comparing it to the source meaning representation only.
Our method outperforms traditional metrics and a constant baseline in most
respects; we also show that synthetic data helps to increase correlation
results by 21% compared to the base system. Our results are comparable to
results obtained in similar QE tasks despite the more challenging setting.Comment: Accepted as a regular paper to 1st Workshop on Learning to Generate
Natural Language (LGNL), Sydney, 10 August 201