Evaluating Sentence Alignment Methods in a Low-Resource Setting: An English-YorùBá Study Case

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00138715" target="_blank" >RIV/00216224:14330/23:00138715 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2023.loresmt-1.10.pdf" target="_blank" >https://aclanthology.org/2023.loresmt-1.10.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2023.loresmt-1.10" target="_blank" >10.18653/v1/2023.loresmt-1.10</a>

Result language
angličtina
Original language name
Evaluating Sentence Alignment Methods in a Low-Resource Setting: An English-YorùBá Study Case
Original language description
Parallel corpora are still crucial to train effective Machine Translation systems. This is even more true for low-resource language pairs, for which Neural Machine Translation has been shown to be less robust to domain mismatch and noise. Due to time and resource constraints, parallel corpora are mostly created with sentence alignment methods which automatically infer alignments. Recent work focused on state-of-the-art pre-trained sentence embeddings-based methods which are available only for a tiny fraction of the world’s languages. In this paper, we evaluate the performance of four widely used algorithms on the low-resource English-Yorùbá language pair against a multidomain benchmark parallel corpus on two experiments involving 1-to-1 alignments with and without reordering. We find that, at least for this language pair, earlier and simpler methods are more suited to the task, all the while not requiring additional data or resources. We also report that the methods we evaluated perform differently across distinct domains, thus indicating that some approach may be better for a specific domain or textual structure.
Czech name
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Czech description
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Project
<a href="/en/project/LM2023062" target="_blank" >LM2023062: Digital Research Infrastructure for Language Technologies, Arts and Humanities</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023)
ISBN
9781959429555
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
123-129
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsburg, PA 18360
Event location
Dubrovnik
Event date
May 6, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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