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Combining translation memories and statistical machine translation using sparse features

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Machine Translation

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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Notes

  1. Henceforth, this method is referred to as “TM combination”.

  2. An HD fragment is composed of a head node and all of its dependents in a dependency tree.

  3. In our experiments, we use the training corpus of our SMT experiments as a TM.

  4. http://ipsc.jrc.ec.europa.eu/index.php?id=198.

  5. https://ec.europa.eu/jrc/en/language-technologies/jrc-acquis.

  6. Unfortunately, due to confidentiality agreements the data used in these experiments cannot be publicly released.

  7. http://computing.dcu.ie/~liangyouli.

  8. 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.

  9. https://github.com/jhclark/multeval.

  10. 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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Authors and Affiliations

  1. ADAPT Centre, School of Computing, Dublin City University, Dublin, Ireland

    Liangyou Li, Andy Way & Qun Liu

  2. ADAPT Centre, SALIS/CTTS, Dublin City University, Dublin, Ireland

    Carla Parra Escartín

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  1. Liangyou Li
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  2. Carla Parra Escartín
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  3. Andy Way
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  4. Qun Liu
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Correspondence to Liangyou Li.

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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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