Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study

Tao Ge, Xingxing Zhang, Furu Wei, Ming Zhou
<span title="">2019</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="" style="color: black;">Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</a> </i> &nbsp;
Sequence-to-sequence (seq2seq) models have achieved tremendous success in text generation tasks. However, there is no guarantee that they can always generate sentences without grammatical errors. In this paper, we present a preliminary empirical study on whether and how much automatic grammatical error correction can help improve seq2seq text generation. We conduct experiments across various seq2seq text generation tasks including machine translation, formality style transfer, sentence
more &raquo; ... on and simplification. Experiments show the state-of-the-art grammatical error correction system can improve the grammaticality of generated text and can bring taskoriented improvements in the tasks where target sentences are in a formal style.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.18653/v1/p19-1609</a> <a target="_blank" rel="external noopener" href="">dblp:conf/acl/GeZWZ19</a> <a target="_blank" rel="external noopener" href="">fatcat:ww5lherf4fau5mxmjrcta3kk64</a> </span>
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