Abstract
In this paper, we present a novel data selection approach based on semi-supervised recursive autoencoders. The model is trained to capture the domain specific features and used for detecting sentences, which are relevant to a specific domain, from a large general-domain corpus. The selected data are used for adapting the built language model and translation model to target domain. Experiments were conducted on an in-domain (IWSLT2014 Chinese-English TED Talk) and a general-domain corpus (UM-Corpus). We evaluated the proposed data selection model in both intrinsic and extrinsic evaluations to investigate the selection successful rate (F-score) of pseudo data, as well as the translation quality (BLEU score) of adapting SMT systems. Empirical results reveal the proposed approach outperforms the state-of-the-art selection approach.
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Lu, Y., Wong, D.F., Chao, L.S., Wang, L. (2014). Data Selection via Semi-supervised Recursive Autoencoders for SMT Domain Adaptation. In: Shi, X., Chen, Y. (eds) Machine Translation. CWMT 2014. Communications in Computer and Information Science, vol 493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45701-6_2
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DOI: https://doi.org/10.1007/978-3-662-45701-6_2
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