@inproceedings{wang-etal-2019-harnessing,
title = "Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer",
author = "Wang, Yunli and
Wu, Yu and
Mou, Lili and
Li, Zhoujun and
Chao, Wenhan",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1365",
doi = "10.18653/v1/D19-1365",
pages = "3573--3578",
abstract = "Formality text style transfer plays an important role in various NLP applications, such as non-native speaker assistants and child education. Early studies normalize informal sentences with rules, before statistical and neural models become a prevailing method in the field. While a rule-based system is still a common preprocessing step for formality style transfer in the neural era, it could introduce noise if we use the rules in a naive way such as data preprocessing. To mitigate this problem, we study how to harness rules into a state-of-the-art neural network that is typically pretrained on massive corpora. We propose three fine-tuning methods in this paper and achieve a new state-of-the-art on benchmark datasets",
}
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<abstract>Formality text style transfer plays an important role in various NLP applications, such as non-native speaker assistants and child education. Early studies normalize informal sentences with rules, before statistical and neural models become a prevailing method in the field. While a rule-based system is still a common preprocessing step for formality style transfer in the neural era, it could introduce noise if we use the rules in a naive way such as data preprocessing. To mitigate this problem, we study how to harness rules into a state-of-the-art neural network that is typically pretrained on massive corpora. We propose three fine-tuning methods in this paper and achieve a new state-of-the-art on benchmark datasets</abstract>
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%0 Conference Proceedings
%T Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer
%A Wang, Yunli
%A Wu, Yu
%A Mou, Lili
%A Li, Zhoujun
%A Chao, Wenhan
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F wang-etal-2019-harnessing
%X Formality text style transfer plays an important role in various NLP applications, such as non-native speaker assistants and child education. Early studies normalize informal sentences with rules, before statistical and neural models become a prevailing method in the field. While a rule-based system is still a common preprocessing step for formality style transfer in the neural era, it could introduce noise if we use the rules in a naive way such as data preprocessing. To mitigate this problem, we study how to harness rules into a state-of-the-art neural network that is typically pretrained on massive corpora. We propose three fine-tuning methods in this paper and achieve a new state-of-the-art on benchmark datasets
%R 10.18653/v1/D19-1365
%U https://aclanthology.org/D19-1365
%U https://doi.org/10.18653/v1/D19-1365
%P 3573-3578
Markdown (Informal)
[Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer](https://aclanthology.org/D19-1365) (Wang et al., EMNLP-IJCNLP 2019)
ACL
- Yunli Wang, Yu Wu, Lili Mou, Zhoujun Li, and Wenhan Chao. 2019. Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3573–3578, Hong Kong, China. Association for Computational Linguistics.