Abstract
The combination of translation memories (TMs) and statistical machine translation (SMT) has been demonstrated to be beneficial. In this paper, we present a combination approach which integrates TMs into SMT by using sparse features extracted at run-time during decoding. These features can be used on both phrase-based SMT and syntax-based SMT. We conducted experiments on a publicly available English–French data set and an English–Spanish industrial data set. Our experimental results show that these features significantly improve our phrase-based and syntax-based SMT baselines on both language pairs.
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Henceforth, this method is referred to as “TM combination”.
An HD fragment is composed of a head node and all of its dependents in a dependency tree.
In our experiments, we use the training corpus of our SMT experiments as a TM.
Unfortunately, due to confidentiality agreements the data used in these experiments cannot be publicly released.
Three probabilities in model III in Wang et al. (2013) which bring the best performance in their paper: \(p(TCM\mid SCM,NLN,LTC,SPL,SEP,Z)\), \(p(LTC\mid CSS,SCM,NLN,SEP,Z)\), \(p(CPM\mid TCM,SCM,NLN,Z)\). Note that our features are the combination of feature names and values in Wang et al. (2013). For example, the feature TCM\(_L\) in our system means that the value of the feature TCM in Wang et al. (2013) is L.
A qualitative analysis of our test set is being done to determine the real impact of our approach.
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Acknowledgements
This research has received funding from the People Programme (Marie Curie Actions) of the European Union’s Framework Programme (FP7/2007-2013) under REA Grant agreement \(\hbox {n}^{\mathrm{o}}\) 317471. The ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.
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Li, L., Parra Escartín, C., Way, A. et al. Combining translation memories and statistical machine translation using sparse features. Machine Translation 30, 183–202 (2016). https://doi.org/10.1007/s10590-016-9187-6
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DOI: https://doi.org/10.1007/s10590-016-9187-6