106 research outputs found

    Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data

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    This paper presents the systems submitted by the University of Groningen to the English-Kazakh language pair (both translation directions) for the WMT 2019 news translation task. We explore the potential benefits of (i) morphological segmentation (both unsupervised and rule-based), given the agglutinative nature of Kazakh, (ii) data from two additional languages (Turkish and Russian), given the scarcity of English-Kazakh data and (iii) synthetic data, both for the source and for the target language. Our best sub- missions ranked second for Kazakh-English and third for English-Kazakh in terms of the BLEU automatic evaluation metric

    Findings of the 2019 Conference on Machine Translation (WMT19)

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    This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation

    On understanding character-level models for representing morphology

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    Morphology is the study of how words are composed of smaller units of meaning (morphemes). It allows humans to create, memorize, and understand words in their language. To process and understand human languages, we expect our computational models to also learn morphology. Recent advances in neural network models provide us with models that compose word representations from smaller units like word segments, character n-grams, or characters. These so-called subword unit models do not explicitly model morphology yet they achieve impressive performance across many multilingual NLP tasks, especially on languages with complex morphological processes. This thesis aims to shed light on the following questions: (1) What do subword unit models learn about morphology? (2) Do we still need prior knowledge about morphology? (3) How do subword unit models interact with morphological typology? First, we systematically compare various subword unit models and study their performance across language typologies. We show that models based on characters are particularly effective because they learn orthographic regularities which are consistent with morphology. To understand which aspects of morphology are not captured by these models, we compare them with an oracle with access to explicit morphological analysis. We show that in the case of dependency parsing, character-level models are still poor in representing words with ambiguous analyses. We then demonstrate how explicit modeling of morphology is helpful in such cases. Finally, we study how character-level models perform in low resource, cross-lingual NLP scenarios, whether they can facilitate cross-linguistic transfer of morphology across related languages. While we show that cross-lingual character-level models can improve low-resource NLP performance, our analysis suggests that it is mostly because of the structural similarities between languages and we do not yet find any strong evidence of crosslinguistic transfer of morphology. This thesis presents a careful, in-depth study and analyses of character-level models and their relation to morphology, providing insights and future research directions on building morphologically-aware computational NLP models

    Survey of Low-Resource Machine Translation

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    International audienceWe present a survey covering the state of the art in low-resource machine translation (MT) research. There are currently around 7,000 languages spoken in the world and almost all language pairs lack significant resources for training machine translation models. There has been increasing interest in research addressing the challenge of producing useful translation models when very little translated training data is available. We present a summary of this topical research field and provide a description of the techniques evaluated by researchers in several recent shared tasks in low-resource MT
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