Spoken Term Discovery for Language Documentation using Translations

Anastasopoulos, Antonios; Bansal, Sameer; Chiang, David; Goldwater, Sharon; Lopez, Adam

doi:10.18653/v1/w17-4607

“…Most work on information assimilation (Bérard et al, 2020;Earle et al, 2012;Brownstein et al, 2008). Few work on dissemination due to small data, less funding, few experts and limited writing system (Östling and Tiedemann, 2017;Zoph et al, 2016;Anastasopoulos et al, 2017;Adams et al, 2017;.…”

Section: Information Disseminationmentioning

confidence: 99%

Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Text-based Translation

Zhou¹,

Waibel²

2021

Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

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We translate a closed text that is known in advance into a severely low resource language by leveraging massive source parallelism. In other words, given a text in 124 source languages, we translate it into a severely low resource language using only ∼1,000 lines of low resource data without any external help. Firstly, we propose a systematic method to rank and choose source languages that are close to the low resource language. We call the linguistic definition of language family Family of Origin (FAMO), and we call the empirical definition of higher-ranked languages using our metrics Family of Choice (FAMC). Secondly, we build an Iteratively Pretrained Multilingual Orderpreserving Lexiconized Transformer (IPML) to train on ∼1,000 lines (∼3.5%) of low resource data. To translate named entities correctly, we build a massive lexicon table for 2,939 Bible named entities in 124 source languages, and include many that occur once and covers more than 66 severely low resource languages. Moreover, we also build a novel method of combining translations from different source languages into one. Using English as a hypothetical low resource language, we get a +23.9 BLEU increase over a multilingual baseline, and a +10.3 BLEU increase over our asymmetric baseline in the Bible dataset. We get a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset. We also have good results for a real severely low resource Mayan language, Eastern Pokomchi.

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“…Most work on information assimilation (Bérard et al, 2020;Earle et al, 2012;Brownstein et al, 2008). Few work on dissemination due to small data, less funding, few experts and limited writing system (Östling and Tiedemann, 2017;Zoph et al, 2016;Anastasopoulos et al, 2017;Adams et al, 2017;.…”

Section: Information Disseminationmentioning

confidence: 99%

Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

2021

0

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SIGTYP 2021 is the third edition of the workshop for typology-related research and its integration into multilingual Natural Language Processing (NLP). The workshop is co-located with the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021), which takes place virtually this year. Our workshop includes a shared task on robust language identification from speech.The final program of SIGTYP contains 4 keynote talks, 3 shared task papers, 10 archival papers, and 14 extended abstracts. This workshop would not have been possible without the contribution of its program committee, to whom we would like to express our gratitude. We should also thank Claire Bowern, Miryam de Lhoneux, Johannes Bjerva, and David Yarowsky for kindly accepting our invitation as invited speakers. The workshop is generously sponsored by Google.

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“…Most work on information assimilation (Bérard et al, 2020;Earle et al, 2012;Brownstein et al, 2008). Few work on dissemination due to small data, less funding, few experts and limited writing system ( Östling and Tiedemann, 2017;Zoph et al, 2016;Anastasopoulos et al, 2017;Adams et al, 2017;.…”

Section: Information Disseminationmentioning

confidence: 99%

Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Machine Translation

Zhou¹,

Waibel²

2021

Preprint

0

View full text Add to dashboard Cite

We translate a closed text that is known in advance into a severely low resource language by leveraging massive source parallelism. In other words, given a text in 124 source languages, we translate it into a severely low resource language using only ∼1,000 lines of low resource data without any external help. Firstly, we propose a systematic method to rank and choose source languages that are close to the low resource language. We call the linguistic definition of language family Family of Origin (FAMO), and we call the empirical definition of higher-ranked languages using our metrics Family of Choice (FAMC). Secondly, we build an Iteratively Pretrained Multilingual Orderpreserving Lexiconized Transformer (IPML) to train on ∼1,000 lines (∼3.5%) of low resource data. To translate named entities correctly, we build a massive lexicon table for 2,939 Bible named entities in 124 source languages, and include many that occur once and covers more than 66 severely low resource languages. Moreover, we also build a novel method of combining translations from different source languages into one. Using English as a hypothetical low resource language, we get a +23.9 BLEU increase over a multilingual baseline, and a +10.3 BLEU increase over our asymmetric baseline in the Bible dataset. We get a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset. We also have good results for a real severely low resource Mayan language, Eastern Pokomchi.

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Spoken Term Discovery for Language Documentation using Translations

Cited by 11 publications

References 11 publications

Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Text-based Translation

Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Text-based Translation

Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Machine Translation

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