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        <title>Metric Learning in Multilingual Sentence Similarity Measurement for Document Alignment</title>
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        <namePart type="given">Surangika</namePart>
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    <abstract>Document alignment techniques based on multilingual sentence representations have recently shown state of the art results. However, these techniques rely on unsupervised distance measurement techniques, which cannot be fined-tuned to the task at hand. In this paper, instead of these unsupervised distance measurement techniques, we employ Metric Learning to derive task-specific distance measurements. These measurements are supervised, meaning that the distance measurement metric is trained using a parallel dataset. Using a dataset belonging to English, Sinhala, and Tamil, which belong to three different language families, we show that these task-specific supervised distance learning metrics outperform their unsupervised counterparts, for document alignment.</abstract>
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%0 Conference Proceedings
%T Metric Learning in Multilingual Sentence Similarity Measurement for Document Alignment
%A Rajitha, Charith
%A Piyarathna, Lakmali
%A Sachintha, Dilan
%A Ranathunga, Surangika
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F rajitha-etal-2021-metric
%X Document alignment techniques based on multilingual sentence representations have recently shown state of the art results. However, these techniques rely on unsupervised distance measurement techniques, which cannot be fined-tuned to the task at hand. In this paper, instead of these unsupervised distance measurement techniques, we employ Metric Learning to derive task-specific distance measurements. These measurements are supervised, meaning that the distance measurement metric is trained using a parallel dataset. Using a dataset belonging to English, Sinhala, and Tamil, which belong to three different language families, we show that these task-specific supervised distance learning metrics outperform their unsupervised counterparts, for document alignment.
%U https://aclanthology.org/2021.ranlp-1.129
%P 1150-1157

Markdown (Informal)

[Metric Learning in Multilingual Sentence Similarity Measurement for Document Alignment](https://aclanthology.org/2021.ranlp-1.129) (Rajitha et al., RANLP 2021)

• Metric Learning in Multilingual Sentence Similarity Measurement for Document Alignment (Rajitha et al., RANLP 2021)

ACL

• Charith Rajitha, Lakmali Piyarathna, Dilan Sachintha, and Surangika Ranathunga. 2021. Metric Learning in Multilingual Sentence Similarity Measurement for Document Alignment. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1150–1157, Held Online. INCOMA Ltd..