@inproceedings{li-etal-2019-laix,
title = "The {LAIX} Systems in the {BEA}-2019 {GEC} Shared Task",
author = "Li, Ruobing and
Wang, Chuan and
Zha, Yefei and
Yu, Yonghong and
Guo, Shiman and
Wang, Qiang and
Liu, Yang and
Lin, Hui",
editor = "Yannakoudakis, Helen and
Kochmar, Ekaterina and
Leacock, Claudia and
Madnani, Nitin and
Pil{\'a}n, Ildik{\'o} and
Zesch, Torsten",
booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4416",
doi = "10.18653/v1/W19-4416",
pages = "159--167",
abstract = "In this paper, we describe two systems we developed for the three tracks we have participated in the BEA-2019 GEC Shared Task. We investigate competitive classification models with bi-directional recurrent neural networks (Bi-RNN) and neural machine translation (NMT) models. For different tracks, we use ensemble systems to selectively combine the NMT models, the classification models, and some rules, and demonstrate that an ensemble solution can effectively improve GEC performance over single systems. Our GEC systems ranked the first in the Unrestricted Track, and the third in both the Restricted Track and the Low Resource Track.",
}
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%0 Conference Proceedings
%T The LAIX Systems in the BEA-2019 GEC Shared Task
%A Li, Ruobing
%A Wang, Chuan
%A Zha, Yefei
%A Yu, Yonghong
%A Guo, Shiman
%A Wang, Qiang
%A Liu, Yang
%A Lin, Hui
%Y Yannakoudakis, Helen
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Zesch, Torsten
%S Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F li-etal-2019-laix
%X In this paper, we describe two systems we developed for the three tracks we have participated in the BEA-2019 GEC Shared Task. We investigate competitive classification models with bi-directional recurrent neural networks (Bi-RNN) and neural machine translation (NMT) models. For different tracks, we use ensemble systems to selectively combine the NMT models, the classification models, and some rules, and demonstrate that an ensemble solution can effectively improve GEC performance over single systems. Our GEC systems ranked the first in the Unrestricted Track, and the third in both the Restricted Track and the Low Resource Track.
%R 10.18653/v1/W19-4416
%U https://aclanthology.org/W19-4416
%U https://doi.org/10.18653/v1/W19-4416
%P 159-167
Markdown (Informal)
[The LAIX Systems in the BEA-2019 GEC Shared Task](https://aclanthology.org/W19-4416) (Li et al., BEA 2019)
ACL
- Ruobing Li, Chuan Wang, Yefei Zha, Yonghong Yu, Shiman Guo, Qiang Wang, Yang Liu, and Hui Lin. 2019. The LAIX Systems in the BEA-2019 GEC Shared Task. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 159–167, Florence, Italy. Association for Computational Linguistics.