Subject: Cross-lingual AMR, AMR Parsing, AMR-to-Text Generation, Multilingual AMR, Cosine Similarity, BLEU, ROUGE, LASER, LaBSE, Distiluse, Low-resource Languages, Europarl
Year: 2022
Type: Proceedings
Title: xAMR: Cross-lingual AMR End-to-End Pipeline
Author: Mitreska, Maja
Author: Pavlov, Tashko
Author: Mishev, Kostadin
Author: Simjanoska, Monika
Abstract: Creating multilingual end-to-end AMR models requires a large amount of cross-lingual data making the parsing and generating tasks exceptionally challenging when dealing with low-resource languages. To avoid this obstacle, this paper presents a cross-lingual AMR (xAMR) pipeline that incorporates the intuitive translation approach to and from the English language as a baseline for further utilization of the AMR parsing and generation models. The proposed pipeline has been evaluated via the cosine similarity of multiple state-of-the-art sentence embeddings used for representing the original and the output sentences generated by our xAMR approach. Also, BLEU and ROUGE scores were used to evaluate the preserved syntax and the word order. xAMR results were compared to multilingual AMR models’ performance for the languages experimented within this research. The results showed that our xAMR outperforms the multilingual approach for all the languages discussed in the paper and can be used as an alternative approach for abstract meaning representation of low-resource languages.
Publisher:
Relation: DeLTA 2022 - 3rd International Conference on Deep Learning Theory and Applications
Identifier: oai:repository.ukim.mk:20.500.12188/27485
Identifier: http://hdl.handle.net/20.500.12188/27485
