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Exploiting bilingual lexicons to improve multilingual embedding-based document and sentence alignment for low-resource languages

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Abstract

Neural machine translation systems trained on low-resource languages produce sub-optimal results due to the scarcity of large parallel datasets. To alleviate this problem, parallel corpora can be mined from the web. Two key tasks in a parallel corpus mining pipeline are web document alignment and sentence alignment. Effective approaches for these tasks obtained vector representations of the documents (or sentences) belonging to the two languages and determine the alignment between the documents (or sentences) based on a semantic similarity scoring mechanism. Recently, document or sentence representations obtained from pre-trained multilingual language models (PMLMs) such as LASER, XLM-R and LaBSE have significantly improved the benchmark scores in diverse natural language processing tasks. In this study, we carry out an empirical analysis of the effectiveness of these PMLMs of the document and sentence alignment tasks in the context of the low-resource language pairs Sinhala–English, Tamil–English and Sinhala–Tamil. Further, we introduce a weighting mechanism based on small-scale bilingual lexicons to improve the semantic similarity measurement between sentences and documents. Our results show that both document and sentence alignment can be further improved using our weighting mechanism. We have also compiled a gold-standard evaluation benchmark dataset for document alignment and sentence alignment tasks for the considered language pairs. This dataset (https://github.com/kdissa/comparable-corpus) and the source code (https://github.com/nlpcuom/parallel_corpus_mining) are publicly released.

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Notes

  1. https://github.com/kdissa/comparable-corpus.

  2. mBERT was not considered since it does not include Sinhala.

  3. http://www.hirunews.lk.

  4. https://www.newsfirst.lk/.

  5. https://www.army.lk/.

  6. https://www.itnnews.lk.

  7. http://www.statmt.org/wmt20/translation-task.html.

  8. https://www.languagesdept.gov.lk/.

  9. We use k = 4 for all experiments in this work as it gave the best results in all our experiments.

  10. https://uom.lk/nlp

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Acknowledgements

Aloka Fernando was initially funded by the Accelerating Higher Education Expansion and Development (AHEAD) Operation of the Ministry of Education, Sri Lanka, funded by the World Bank. Currently, she is funded by a Senate Research Committee (SRC) grant from the University of Moratuwa, Sri Lanka. Dataset creation was funded by an SRC grant from University of Moratuwa, Sri Lanka.

Funding

Funding was provided by Higher Education Expansion and Development (AHEAD) and Senate Research Committee (SRC) Grant University of Moratuwa.

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  1. Aloka Fernando, Dilan Sachintha, Lakmali Piyarathna and Charith Rajitha have contributed equally to this work.

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  1. Department of Computer Science and Engineering, University of Moratuwa, Katubedda, Sri Lanka

    Aloka Fernando, Surangika Ranathunga, Dilan Sachintha, Lakmali Piyarathna & Charith Rajitha

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Appendices

Appendix A Algorithms for using bilingual lexicons

Our improvement to the document alignment and sentence alignment algorithms consider bilingual lexicons as explained in Sects. 4.1.1 and 4.2.2, respectively. The supporting algorithms related to term matching using person names (Algorithm 1) and rest of the bilingual lexicons (Algorithm 2) are shown below.

figure i

Appendix B Document alignment results

Table 11 shows the document alignment results for each news source for the language pairs English–Sinhala, English–Tamil and Sinhala–Tamil. In Table 7, the individual scores obtained for the news sources are averaged. The score in bold is the result corresponding to the best F1 score with respective to the news source and language pair.

Table 11 Document Alignment results in terms of recall (R), precision (P) and F1 with respective to each language pair. Here, BL refers to the recreated Baseline [13] considering LASER embeddings. On top of this, each bilingual lexicon had been added and the experiments were repeated. The bilingual lexicons considered were Person Names (N), Designations (Ds), Dictionary (Dc) and Improved Dictionary (MDc). Subsequently considering PMLMs XLM-R and LaBSE, the same set of experiments have been conducted.
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Fernando, A., Ranathunga, S., Sachintha, D. et al. Exploiting bilingual lexicons to improve multilingual embedding-based document and sentence alignment for low-resource languages. Knowl Inf Syst 65, 571–612 (2023). https://doi.org/10.1007/s10115-022-01761-x

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