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        <title>On Pronunciations in Wiktionary: Extraction and Experiments on Multilingual Syllabification and Stress Prediction</title>
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        <namePart type="given">Winston</namePart>
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    <abstract>We constructed parsers for five non-English editions of Wiktionary, which combined with pronunciations from the English edition, comprises over 5.3 million IPA pronunciations, the largest pronunciation lexicon of its kind. This dataset is a unique comparable corpus of IPA pronunciations annotated from multiple sources. We analyze the dataset, noting the presence of machine-generated pronunciations. We develop a novel visualization method to quantify syllabification. We experiment on the new combined task of multilingual IPA syllabification and stress prediction, finding that training a massively multilingual neural sequence-to-sequence model with copy attention can improve performance on both high- and low-resource languages, and multi-task training on stress prediction helps with syllabification.</abstract>
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            <start>68</start>
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%0 Conference Proceedings
%T On Pronunciations in Wiktionary: Extraction and Experiments on Multilingual Syllabification and Stress Prediction
%A Wu, Winston
%A Yarowsky, David
%S Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021)
%D 2021
%8 sep
%I INCOMA Ltd.
%C Online (Virtual Mode)
%F wu-yarowsky-2021-pronunciations
%X We constructed parsers for five non-English editions of Wiktionary, which combined with pronunciations from the English edition, comprises over 5.3 million IPA pronunciations, the largest pronunciation lexicon of its kind. This dataset is a unique comparable corpus of IPA pronunciations annotated from multiple sources. We analyze the dataset, noting the presence of machine-generated pronunciations. We develop a novel visualization method to quantify syllabification. We experiment on the new combined task of multilingual IPA syllabification and stress prediction, finding that training a massively multilingual neural sequence-to-sequence model with copy attention can improve performance on both high- and low-resource languages, and multi-task training on stress prediction helps with syllabification.
%U https://aclanthology.org/2021.bucc-1.9
%P 68-74

Markdown (Informal)

[On Pronunciations in Wiktionary: Extraction and Experiments on Multilingual Syllabification and Stress Prediction](https://aclanthology.org/2021.bucc-1.9) (Wu & Yarowsky, BUCC 2021)

• On Pronunciations in Wiktionary: Extraction and Experiments on Multilingual Syllabification and Stress Prediction (Wu & Yarowsky, BUCC 2021)

ACL

• Winston Wu and David Yarowsky. 2021. On Pronunciations in Wiktionary: Extraction and Experiments on Multilingual Syllabification and Stress Prediction. In Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021), pages 68–74, Online (Virtual Mode). INCOMA Ltd..