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1
Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation ...
Abstract: We demonstrate a new approach to Neural Machine Translation (NMT) for low-resource languages using a ubiquitous linguistic resource, Interlinear Glossed Text (IGT). IGT represents a non-English sentence as a sequence of English lemmas and morpheme labels. As such, it can serve as a pivot or interlingua for NMT. Our contribution is four-fold. Firstly, we pool IGT for 1,497 languages in ODIN (54,545 glosses) and 70,918 glosses in Arapaho and train a gloss-to-target NMT system from IGT to English, with a BLEU score of 25.94. We introduce a multilingual NMT model that tags all glossed text with gloss-source language tags and train a universal system with shared attention across 1,497 languages. Secondly, we use the IGT gloss-to-target translation as a key step in an English-Turkish MT system trained on only 865 lines from ODIN. Thirdly, we we present five metrics for evaluating extremely low-resource translation when BLEU is no longer sufficient and evaluate the Turkish low-resource system using BLEU and also ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1911.02709
https://arxiv.org/abs/1911.02709
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2
A Resource for Computational Experiments on Mapudungun ...
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3
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations ...
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4
Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning ...
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5
Statistical modality tagging from rule-based annotations and crowdsourcing ...
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6
Use of Modality and Negation in Semantically-Informed Syntactic MT ...
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7
A Modality Lexicon and its use in Automatic Tagging ...
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8
Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ...
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9
Discourse Processing of Dialogues with Multiple Threads ...
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