Abstract
In statistical machine translation, training data usually have the characteristics of diverse sources, multiple themes, different genre, and are often not in accordance with the domain of target text to be translated, resulting in domain adaptive problem. The existing adaptive methods for statistical machine translation aim for the target text and focus on the selection of training data and the adjustment of translation models. These approaches have not specified explicit domain labels for texts or data. This study gives explicit domain labels and uses two examples for specific context knowledge, (1) Domain knowledge based on Chinese Thesaurus are applied to assign domain labels of Chinese Library Classification Number to Chinese texts; (2) Two-dimensional lexicalized domain knowledge, such as Semantic Category and Application Scenarios, is used to label Japanese sentence. Based on the obtained domain labels for development data and test data, the training data can be filtered to achieve the goal of domain consistency. Experiments show that only a part of the training data can gain a comparable translation performance to the whole training data. This shows that the method is efficient and feasible.
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Acknowledgments
This research work was partially supported by National Natural Science of China (61303152, 71503240), and ISTIC Research Foundation Projects (ZD2016-05).
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He, Y., Ding, L., Li, Y. (2016). Research on Domain Adaptation for SMT Based on Specific Domain Knowledge. In: Yang, M., Liu, S. (eds) Machine Translation. CWMT 2016. Communications in Computer and Information Science, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-3635-4_5
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DOI: https://doi.org/10.1007/978-981-10-3635-4_5
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