Boito, M.Z., Villavicencio, A. orcid.org/0000-0002-3731-9168 and Besacier, L. (2020) Investigating language impact in bilingual approaches for computational language documentation. In: Beermann, D., Besacier, L., Sakti, S. and Soria, C., (eds.) 1st Joint Workshop of Spoken Language Technologies for Under-resourced languages and Collaboration and Computing for Under-Resourced Languages (SLTU-CCURL 2020). 1st Joint SLTU and CCURL Workshop 2020, 10-12 May 2020, Marseille, France. Special Interest Group: Under-resourced Languages (SIGUL) , pp. 79-87. ISBN 979-10-95546-35-1
Abstract
For endangered languages, data collection campaigns have to accommodate the challenge that many of them are from oral tradition, and producing transcriptions is costly. Therefore, it is fundamental to translate them into a widely spoken language to ensure interpretability of the recordings. In this paper we investigate how the choice of translation language affects the posterior documentation work and potential automatic approaches which will work on top of the produced bilingual corpus. For answering this question, we use the MaSS multilingual speech corpus (Boito et al., 2020) for creating 56 bilingual pairs that we apply to the task of low-resource unsupervised word segmentation and alignment. Our results highlight that the choice of language for translation influences the word segmentation performance, and that different lexicons are learned by using different aligned translations. Lastly, this paper proposes a hybrid approach for bilingual word segmentation, combining boundary clues extracted from a non-parametric Bayesian model (Goldwater et al., 2009a) with the attentional word segmentation neural model from Godard et al. (2018). Our results suggest that incorporating these clues into the neural models’ input representation increases their translation and alignment quality, specially for challenging language pairs.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2020 European Language Resources Association (ELRA). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial Licence (https://creativecommons.org/licenses/by-nc/4.0/) |
Keywords: | word segmentation; sequence-to-sequence models; computational language documentation; attention mechanism |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number National Council for Scientific and Technological Development N/A |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 31 Mar 2020 09:02 |
Last Modified: | 21 Jun 2023 13:23 |
Published Version: | https://aclanthology.org/2020.sltu-1 |
Status: | Published |
Publisher: | Special Interest Group: Under-resourced Languages (SIGUL) |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158907 |