@inproceedings{mallinson-etal-2020-zero,
title = "Zero-Shot Crosslingual Sentence Simplification",
author = "Mallinson, Jonathan and
Sennrich, Rico and
Lapata, Mirella",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.415/",
doi = "10.18653/v1/2020.emnlp-main.415",
pages = "5109--5126",
abstract = "Sentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with encoder-decoder models trained on large amounts of parallel data which often only exists in English. We propose a zero-shot modeling framework which transfers simplification knowledge from English to another language (for which no parallel simplification corpus exists) while generalizing across languages and tasks. A shared transformer encoder constructs language-agnostic representations, with a combination of task-specific encoder layers added on top (e.g., for translation and simplification). Empirical results using both human and automatic metrics show that our approach produces better simplifications than unsupervised and pivot-based methods."
}
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<abstract>Sentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with encoder-decoder models trained on large amounts of parallel data which often only exists in English. We propose a zero-shot modeling framework which transfers simplification knowledge from English to another language (for which no parallel simplification corpus exists) while generalizing across languages and tasks. A shared transformer encoder constructs language-agnostic representations, with a combination of task-specific encoder layers added on top (e.g., for translation and simplification). Empirical results using both human and automatic metrics show that our approach produces better simplifications than unsupervised and pivot-based methods.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Crosslingual Sentence Simplification
%A Mallinson, Jonathan
%A Sennrich, Rico
%A Lapata, Mirella
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F mallinson-etal-2020-zero
%X Sentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with encoder-decoder models trained on large amounts of parallel data which often only exists in English. We propose a zero-shot modeling framework which transfers simplification knowledge from English to another language (for which no parallel simplification corpus exists) while generalizing across languages and tasks. A shared transformer encoder constructs language-agnostic representations, with a combination of task-specific encoder layers added on top (e.g., for translation and simplification). Empirical results using both human and automatic metrics show that our approach produces better simplifications than unsupervised and pivot-based methods.
%R 10.18653/v1/2020.emnlp-main.415
%U https://aclanthology.org/2020.emnlp-main.415/
%U https://doi.org/10.18653/v1/2020.emnlp-main.415
%P 5109-5126
Markdown (Informal)
[Zero-Shot Crosslingual Sentence Simplification](https://aclanthology.org/2020.emnlp-main.415/) (Mallinson et al., EMNLP 2020)
ACL
- Jonathan Mallinson, Rico Sennrich, and Mirella Lapata. 2020. Zero-Shot Crosslingual Sentence Simplification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5109–5126, Online. Association for Computational Linguistics.