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        <title>Majority Voting with Bidirectional Pre-translation For Bitext Retrieval</title>
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    <name type="personal">
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    <abstract>Obtaining high-quality parallel corpora is of paramount importance for training NMT systems. However, as many language pairs lack adequate gold-standard training data, a popular approach has been to mine so-called “pseudo-parallel” sentences from paired documents in two languages. In this paper, we outline some drawbacks with current methods that rely on an embedding similarity threshold, and propose a heuristic method in its place. Our method involves translating both halves of a paired corpus before mining, and then performing a majority vote on sentence pairs mined in three ways: after translating documents in language x to language y, after translating language y to x, and using the original documents in languages x and y. We demonstrate success with this novel approach on the Tatoeba similarity search benchmark in 64 low-resource languages, and on NMT in Kazakh and Gujarati. We also uncover the effect of resource-related factors (i.e. how much monolingual/bilingual data is available for a given language) on the optimal choice of bitext mining method, demonstrating that there is currently no one-size-fits-all approach for this task. We make the code and data used in our experiments publicly available.</abstract>
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%0 Conference Proceedings
%T Majority Voting with Bidirectional Pre-translation For Bitext Retrieval
%A Jones, Alexander
%A Wijaya, Derry Tanti
%Y Rapp, Reinhard
%Y Sharoff, Serge
%Y Zweigenbaum, Pierre
%S Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Online (Virtual Mode)
%F jones-wijaya-2021-majority
%X Obtaining high-quality parallel corpora is of paramount importance for training NMT systems. However, as many language pairs lack adequate gold-standard training data, a popular approach has been to mine so-called “pseudo-parallel” sentences from paired documents in two languages. In this paper, we outline some drawbacks with current methods that rely on an embedding similarity threshold, and propose a heuristic method in its place. Our method involves translating both halves of a paired corpus before mining, and then performing a majority vote on sentence pairs mined in three ways: after translating documents in language x to language y, after translating language y to x, and using the original documents in languages x and y. We demonstrate success with this novel approach on the Tatoeba similarity search benchmark in 64 low-resource languages, and on NMT in Kazakh and Gujarati. We also uncover the effect of resource-related factors (i.e. how much monolingual/bilingual data is available for a given language) on the optimal choice of bitext mining method, demonstrating that there is currently no one-size-fits-all approach for this task. We make the code and data used in our experiments publicly available.
%U https://aclanthology.org/2021.bucc-1.7
%P 46-59

Markdown (Informal)

[Majority Voting with Bidirectional Pre-translation For Bitext Retrieval](https://aclanthology.org/2021.bucc-1.7) (Jones & Wijaya, BUCC 2021)

• Majority Voting with Bidirectional Pre-translation For Bitext Retrieval (Jones & Wijaya, BUCC 2021)

ACL

• Alexander Jones and Derry Tanti Wijaya. 2021. Majority Voting with Bidirectional Pre-translation For Bitext Retrieval. In Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021), pages 46–59, Online (Virtual Mode). INCOMA Ltd..