xAMR: Cross-lingual AMR End-to-End Pipeline

Maja Mitreska, Tashko Pavlov, Kostadin Mishev, Kostadin Mishev, Monika Simjanoska, Monika Simjanoska

2022

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.

Paper Citation

in Harvard Style

Mitreska M., Pavlov T., Mishev K. and Simjanoska M. (2022). xAMR: Cross-lingual AMR End-to-End Pipeline. In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-584-5, pages 132-139. DOI: 10.5220/0011276500003277

in Bibtex Style

@conference{delta22,
author={Maja Mitreska and Tashko Pavlov and Kostadin Mishev and Monika Simjanoska},
title={xAMR: Cross-lingual AMR End-to-End Pipeline},
booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2022},
pages={132-139},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011276500003277},
isbn={978-989-758-584-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - xAMR: Cross-lingual AMR End-to-End Pipeline
SN - 978-989-758-584-5
AU - Mitreska M.
AU - Pavlov T.
AU - Mishev K.
AU - Simjanoska M.
PY - 2022
SP - 132
EP - 139
DO - 10.5220/0011276500003277