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.- Anthology ID:
- 2020.coling-main.123
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1435–1446
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.123
- DOI:
- 10.18653/v1/2020.coling-main.123
- Cite (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.
- Cite (Informal):
- Multi-Word Lexical Simplification (Przybyła & Shardlow, COLING 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.123.pdf
- Code
- piotrmp/mwls1 + additional community code