Abstract

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 compression and simplification. Experiments show the state-of-the-art grammatical error correction system can improve the grammaticality of generated text and can bring task-oriented improvements in the tasks where target sentences are in a formal style.

Highlights

  • Sequence-to-sequence text generation (Cho et al, 2014; Sutskever et al, 2014) has attracted growing attention in natural language processing (NLP)

  • We propose an empirical study on GEC post editing for various text generation tasks

  • We present an empirical study on GEC post editing for seq2seq text generation

Read more

Summary

Introduction

Sequence-to-sequence (seq2seq) text generation (Cho et al, 2014; Sutskever et al, 2014) has attracted growing attention in natural language processing (NLP). We are curious whether they can help improve seq2seq based natural language generation (NLG) models. We propose an empirical study on GEC post editing for various text generation tasks Experimental results demonstrate that a state-of-the-art GEC system is helpful for improving the grammaticality of generated text and that it can bring task-oriented improvements in the tasks where target sentences are in a formal style. We present an empirical study on GEC post editing for seq2seq text generation. To the best of our knowledge, it is the first work to study improving seq2seq based NLG models using GEC. We show some interesting results by thoroughly comparing and analyzing GEC post editing for various seq2seq text generation tasks, shedding light on the potential of GEC for NLG

Sequence-to-sequence Text Generation
Automatic Grammatical Error Correction
Experiments and Evaluation
Machine translation
Formality style transfer
Sentence compression and simplification
Human Evaluation
Findings
Related Work and Discussion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.