A Nested Attention Neural Hybrid Model for Grammatical Error Correction A Nested Attention Neural Hybrid Model for Grammatical Error Correction
Paper summary Proposing character-based extensions to a neural MT system for grammatical error correction. OOV words are represented in the encoder and decoder using character-based RNNs. They evaluate on the CoNLL-14 dataset, integrate probabilities from a large language model, and achieve good results. https://i.imgur.com/r0Bsxp5.png
doi.org
sci-hub
scholar.google.com
A Nested Attention Neural Hybrid Model for Grammatical Error Correction
Ji, Jianshu and Wang, Qinlong and Toutanova, Kristina and Gong, Yongen and Truong, Steven and Gao, Jianfeng
Association for Computational Linguistics - 2017 via Local Bibsonomy
Keywords: dblp


Summary by Marek Rei 2 months ago
Loading...
Your comment:


ShortScience.org allows researchers to publish paper summaries that are voted on and ranked!
About

Sponsored by: and