@inproceedings{wang-etal-2020-formality,
title = "Formality Style Transfer with Shared Latent Space",
author = "Wang, Yunli and
Wu, Yu and
Mou, Lili and
Li, Zhoujun and
Chao, WenHan",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.203",
doi = "10.18653/v1/2020.coling-main.203",
pages = "2236--2249",
abstract = "Conventional approaches for formality style transfer borrow models from neural machine translation, which typically requires massive parallel data for training. However, the dataset for formality style transfer is considerably smaller than translation corpora. Moreover, we observe that informal and formal sentences closely resemble each other, which is different from the translation task where two languages have different vocabularies and grammars. In this paper, we present a new approach, Sequence-to-Sequence with Shared Latent Space (S2S-SLS), for formality style transfer, where we propose two auxiliary losses and adopt joint training of bi-directional transfer and auto-encoding. Experimental results show that S2S-SLS (with either RNN or Transformer architectures) consistently outperforms baselines in various settings, especially when we have limited data.",
}
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<abstract>Conventional approaches for formality style transfer borrow models from neural machine translation, which typically requires massive parallel data for training. However, the dataset for formality style transfer is considerably smaller than translation corpora. Moreover, we observe that informal and formal sentences closely resemble each other, which is different from the translation task where two languages have different vocabularies and grammars. In this paper, we present a new approach, Sequence-to-Sequence with Shared Latent Space (S2S-SLS), for formality style transfer, where we propose two auxiliary losses and adopt joint training of bi-directional transfer and auto-encoding. Experimental results show that S2S-SLS (with either RNN or Transformer architectures) consistently outperforms baselines in various settings, especially when we have limited data.</abstract>
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%0 Conference Proceedings
%T Formality Style Transfer with Shared Latent Space
%A Wang, Yunli
%A Wu, Yu
%A Mou, Lili
%A Li, Zhoujun
%A Chao, WenHan
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F wang-etal-2020-formality
%X Conventional approaches for formality style transfer borrow models from neural machine translation, which typically requires massive parallel data for training. However, the dataset for formality style transfer is considerably smaller than translation corpora. Moreover, we observe that informal and formal sentences closely resemble each other, which is different from the translation task where two languages have different vocabularies and grammars. In this paper, we present a new approach, Sequence-to-Sequence with Shared Latent Space (S2S-SLS), for formality style transfer, where we propose two auxiliary losses and adopt joint training of bi-directional transfer and auto-encoding. Experimental results show that S2S-SLS (with either RNN or Transformer architectures) consistently outperforms baselines in various settings, especially when we have limited data.
%R 10.18653/v1/2020.coling-main.203
%U https://aclanthology.org/2020.coling-main.203
%U https://doi.org/10.18653/v1/2020.coling-main.203
%P 2236-2249
Markdown (Informal)
[Formality Style Transfer with Shared Latent Space](https://aclanthology.org/2020.coling-main.203) (Wang et al., COLING 2020)
ACL
- Yunli Wang, Yu Wu, Lili Mou, Zhoujun Li, and WenHan Chao. 2020. Formality Style Transfer with Shared Latent Space. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2236–2249, Barcelona, Spain (Online). International Committee on Computational Linguistics.