Abstract
Abstract Meaning Representation (AMR) is a popular formalism of natural language that represents the meaning of a sentence as a semantic graph. It is agnostic about how to derive meanings from strings and for this reason it lends itself well to the encoding of semantics across languages. However, cross-lingual AMR parsing is a hard task, because training data are scarce in languages other than English and the existing English AMR parsers are not directly suited to being used in a cross-lingual setting. In this work we tackle these two problems so as to enable cross-lingual AMR parsing: we explore different transfer learning techniques for producing automatic AMR annotations across languages and develop a cross-lingual AMR parser, XL-AMR. This can be trained on the produced data and does not rely on AMR aligners or source-copy mechanisms as is commonly the case in English AMR parsing. The results of XL-AMR significantly surpass those previously reported in Chinese, German, Italian and Spanish. Finally we provide a qualitative analysis which sheds light on the suitability of AMR across languages. We release XL-AMR at github.com/SapienzaNLP/xl-amr.- Anthology ID:
- 2020.emnlp-main.195
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2487–2500
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.195
- DOI:
- 10.18653/v1/2020.emnlp-main.195
- Cite (ACL):
- Rexhina Blloshmi, Rocco Tripodi, and Roberto Navigli. 2020. XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2487–2500, Online. Association for Computational Linguistics.
- Cite (Informal):
- XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques (Blloshmi et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.emnlp-main.195.pdf
- Code
- SapienzaNLP/xl-amr