Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Grammatical Error Correction with Finite State Transducers   Grammatical error correction (GEC) is one of the areas in natural language
processing in which purely neural models have not yet superseded more
traditional symbolic models. Hybrid systems combining phrase-based statistical
machine translation (SMT) and neural sequence models are currently among the
most effective approaches to GEC. However, both SMT and neural
sequence-to-sequence models require large amounts of annotated data. Language
model based GEC (LM-GEC) is a promising alternative which does not rely on
annotated training data. We show how to improve LM-GEC by applying modelling
techniques based on finite state transducers. We report further gains by
rescoring with neural language models. We show that our methods developed for
LM-GEC can also be used with SMT systems if annotated training data is
available. Our best system outperforms the best published result on the
CoNLL-2014 test set, and achieves far better relative improvements over the SMT
baselines than previous hybrid systems.

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The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: physics
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The categories of this paper are: math-ph
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: eess
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol