@inproceedings{przybyla-shardlow-2020-multi,
title = "Multi-Word Lexical Simplification",
author = "Przyby{\l}a, Piotr and
Shardlow, Matthew",
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.123",
doi = "10.18653/v1/2020.coling-main.123",
pages = "1435--1446",
abstract = "In this work we propose the task of multi-word lexical simplification, in which a sentence in natural language is made easier to understand by replacing its fragment with a simpler alternative, both of which can consist of many words. In order to explore this new direction, we contribute a corpus (MWLS1), including 1462 sentences in English from various sources with 7059 simplifications provided by human annotators. We also propose an automatic solution (Plainifier) based on a purpose-trained neural language model and evaluate its performance, comparing to human and resource-based baselines.",
}
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%0 Conference Proceedings
%T Multi-Word Lexical Simplification
%A Przybyła, Piotr
%A Shardlow, Matthew
%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 przybyla-shardlow-2020-multi
%X In this work we propose the task of multi-word lexical simplification, in which a sentence in natural language is made easier to understand by replacing its fragment with a simpler alternative, both of which can consist of many words. In order to explore this new direction, we contribute a corpus (MWLS1), including 1462 sentences in English from various sources with 7059 simplifications provided by human annotators. We also propose an automatic solution (Plainifier) based on a purpose-trained neural language model and evaluate its performance, comparing to human and resource-based baselines.
%R 10.18653/v1/2020.coling-main.123
%U https://aclanthology.org/2020.coling-main.123
%U https://doi.org/10.18653/v1/2020.coling-main.123
%P 1435-1446
Markdown (Informal)
[Multi-Word Lexical Simplification](https://aclanthology.org/2020.coling-main.123) (Przybyła & Shardlow, COLING 2020)
ACL
- Piotr Przybyła and Matthew Shardlow. 2020. Multi-Word Lexical Simplification. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1435–1446, Barcelona, Spain (Online). International Committee on Computational Linguistics.