Quantifying Synthesis and Fusion and their Impact on Machine Translation

Arturo Oncevay, Duygu Ataman, Niels Van Berkel, Barry Haddow, Alexandra Birch, Johannes Bjerva


Abstract
Theoretical work in morphological typology offers the possibility of measuring morphological diversity on a continuous scale. However, literature in Natural Language Processing (NLP) typically labels a whole language with a strict type of morphology, e.g. fusional or agglutinative. In this work, we propose to reduce the rigidity of such claims, by quantifying morphological typology at the word and segment level. We consider Payne (2017)’s approach to classify morphology using two indices: synthesis (e.g. analytic to polysynthetic) and fusion (agglutinative to fusional). For computing synthesis, we test unsupervised and supervised morphological segmentation methods for English, German and Turkish, whereas for fusion, we propose a semi-automatic method using Spanish as a case study. Then, we analyse the relationship between machine translation quality and the degree of synthesis and fusion at word (nouns and verbs for English-Turkish, and verbs in English-Spanish) and segment level (previous language pairs plus English-German in both directions). We complement the word-level analysis with human evaluation, and overall, we observe a consistent impact of both indexes on machine translation quality.
Anthology ID:
2022.naacl-main.94
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1308–1321
Language:
URL:
https://aclanthology.org/2022.naacl-main.94
DOI:
10.18653/v1/2022.naacl-main.94
Bibkey:
Cite (ACL):
Arturo Oncevay, Duygu Ataman, Niels Van Berkel, Barry Haddow, Alexandra Birch, and Johannes Bjerva. 2022. Quantifying Synthesis and Fusion and their Impact on Machine Translation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1308–1321, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Quantifying Synthesis and Fusion and their Impact on Machine Translation (Oncevay et al., NAACL 2022)
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PDF:
https://aclanthology.org/2022.naacl-main.94.pdf