Domain adaptation strategies in statistical machine translation: a brief overview
نویسندگان
چکیده
منابع مشابه
Improved Domain Adaptation for Statistical Machine Translation
We present a simple and effective infrastructure for domain adaptation for statistical machine translation (MT). To build MT systems for different domains, it trains, tunes and deploys a single translation system that is capable of producing adapted domain translations and preserving the original generic accuracy at the same time. The approach unifies automatic domain detection and domain model...
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In recent years the performance of SMT increased in domains with enough training data. But under real-world conditions, it is often not possible to collect enough parallel data. We propose an approach to adapt an SMT system using small amounts of parallel in-domain data by introducing the corpus identifier (corpus id) as an additional target factor. Then we added features to model the generatio...
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Since the emergence of translation memory software, translation companies and freelance translators have been accumulating translated text for various languages and domains. This data has the potential of being used for training domain-specific machine translation systems for corporate or even personal use. But while the resulting systems usually perform well in translating domain-specific lang...
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The special challenge of the WMT 2007 shared task was domain adaptation. We took this opportunity to experiment with various ways of adapting a statistical machine translation systems to a special domain (here: news commentary), when most of the training data is from a different domain (here: European Parliament speeches). This paper also gives a description of the submission of the University ...
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Mixture modelling is a standard technique for density estimation, but its use in statistical machine translation (SMT) has just started to be explored. One of the main advantages of this technique is its capability to learn specific probability distributions that better fit subsets of the training dataset. This feature is even more important in SMT given the difficulties to translate polysemic ...
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ژورنال
عنوان ژورنال: The Knowledge Engineering Review
سال: 2015
ISSN: 0269-8889,1469-8005
DOI: 10.1017/s0269888915000119