16,558 research outputs found

    Modeling Target-Side Inflection in Neural Machine Translation

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    NMT systems have problems with large vocabulary sizes. Byte-pair encoding (BPE) is a popular approach to solving this problem, but while BPE allows the system to generate any target-side word, it does not enable effective generalization over the rich vocabulary in morphologically rich languages with strong inflectional phenomena. We introduce a simple approach to overcome this problem by training a system to produce the lemma of a word and its morphologically rich POS tag, which is then followed by a deterministic generation step. We apply this strategy for English-Czech and English-German translation scenarios, obtaining improvements in both settings. We furthermore show that the improvement is not due to only adding explicit morphological information.Comment: Accepted as a research paper at WMT17. (Updated version with corrected references.

    The impact of morphological errors in phrase-based statistical machine translation from German and English into Swedish

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    We have investigated the potential for improvement in target language morphology when translating into Swedish from English and German, by measuring the errors made by a state of the art phrase-based statistical machine translation system. Our results show that there is indeed a performance gap to be filled by better modelling of inflectional morphology and compounding; and that the gap is not filled by simply feeding the translation system with more training data

    Using Danish as a CG Interlingua: A Wide-Coverage Norwegian-English Machine Translation System

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    Proceedings of the 16th Nordic Conference of Computational Linguistics NODALIDA-2007. Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit. University of Tartu, Tartu, 2007. ISBN 978-9985-4-0513-0 (online) ISBN 978-9985-4-0514-7 (CD-ROM) pp. 21-28

    Probabilistic Modelling of Morphologically Rich Languages

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    This thesis investigates how the sub-structure of words can be accounted for in probabilistic models of language. Such models play an important role in natural language processing tasks such as translation or speech recognition, but often rely on the simplistic assumption that words are opaque symbols. This assumption does not fit morphologically complex language well, where words can have rich internal structure and sub-word elements are shared across distinct word forms. Our approach is to encode basic notions of morphology into the assumptions of three different types of language models, with the intention that leveraging shared sub-word structure can improve model performance and help overcome data sparsity that arises from morphological processes. In the context of n-gram language modelling, we formulate a new Bayesian model that relies on the decomposition of compound words to attain better smoothing, and we develop a new distributed language model that learns vector representations of morphemes and leverages them to link together morphologically related words. In both cases, we show that accounting for word sub-structure improves the models' intrinsic performance and provides benefits when applied to other tasks, including machine translation. We then shift the focus beyond the modelling of word sequences and consider models that automatically learn what the sub-word elements of a given language are, given an unannotated list of words. We formulate a novel model that can learn discontiguous morphemes in addition to the more conventional contiguous morphemes that most previous models are limited to. This approach is demonstrated on Semitic languages, and we find that modelling discontiguous sub-word structures leads to improvements in the task of segmenting words into their contiguous morphemes.Comment: DPhil thesis, University of Oxford, submitted and accepted 2014. http://ora.ox.ac.uk/objects/uuid:8df7324f-d3b8-47a1-8b0b-3a6feb5f45c

    Translation Alignment and Extraction Within a Lexica-Centered Iterative Workflow

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    This thesis addresses two closely related problems. The first, translation alignment, consists of identifying bilingual document pairs that are translations of each other within multilingual document collections (document alignment); identifying sentences, titles, etc, that are translations of each other within bilingual document pairs (sentence alignment); and identifying corresponding word and phrase translations within bilingual sentence pairs (phrase alignment). The second is extraction of bilingual pairs of equivalent word and multi-word expressions, which we call translation equivalents (TEs), from sentence- and phrase-aligned parallel corpora. While these same problems have been investigated by other authors, their focus has been on fully unsupervised methods based mostly or exclusively on parallel corpora. Bilingual lexica, which are basically lists of TEs, have not been considered or given enough importance as resources in the treatment of these problems. Human validation of TEs, which consists of manually classifying TEs as correct or incorrect translations, has also not been considered in the context of alignment and extraction. Validation strengthens the importance of infrequent TEs (most of the entries of a validated lexicon) that otherwise would be statistically unimportant. The main goal of this thesis is to revisit the alignment and extraction problems in the context of a lexica-centered iterative workflow that includes human validation. Therefore, the methods proposed in this thesis were designed to take advantage of knowledge accumulated in human-validated bilingual lexica and translation tables obtained by unsupervised methods. Phrase-level alignment is a stepping stone for several applications, including the extraction of new TEs, the creation of statistical machine translation systems, and the creation of bilingual concordances. Therefore, for phrase-level alignment, the higher accuracy of human-validated bilingual lexica is crucial for achieving higher quality results in these downstream applications. There are two main conceptual contributions. The first is the coverage maximization approach to alignment, which makes direct use of the information contained in a lexicon, or in translation tables when this is small or does not exist. The second is the introduction of translation patterns which combine novel and old ideas and enables precise and productive extraction of TEs. As material contributions, the alignment and extraction methods proposed in this thesis have produced source materials for three lines of research, in the context of three PhD theses (two of them already defended), all sharing with me the supervision of my advisor. The topics of these lines of research are statistical machine translation, algorithms and data structures for indexing and querying phrase-aligned parallel corpora, and bilingual lexica classification and generation. Four publications have resulted directly from the work presented in this thesis and twelve from the collaborative lines of research
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