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.- Anthology ID:
- 2020.emnlp-main.415
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5109–5126
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.415
- DOI:
- 10.18653/v1/2020.emnlp-main.415
- Cite (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.
- Cite (Informal):
- Zero-Shot Crosslingual Sentence Simplification (Mallinson et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.415.pdf