xAMR: Cross-lingual AMR End-to-End Pipeline
Date Issued
2022
Author(s)
Mitreska, Maja
Pavlov, Tashko
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.
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.
Subjects
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