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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