Haddow, Barry

Hasler, Eva

Koehn, Philipp

We present a new approach to domain adaptation for SMT that enriches standard phrase-based models with lexicalised word and phrase pair features to help the model select appropriate translations for the target domain (TED talks). In addition, we show how source-side sentence-level topics can be incorporated to make the features differentiate between more fine-grained topics within the target domain (topic adaptation). We compare tuning our sparse features on a development set versus on the entire in-domain corpus and introduce a new method of porting them to larger mixed-domain models. Experimental results show that our features improve performance over a MIRA baseline and that in some cases we can get additional improvements with topic features. We evaluate our methods on two language pairs, English-French and German-English, showing promising results

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Sparse lexicalised features and topic adaptation for SMT

https://www.pure.ed.ac.uk/ws/files/18891148/Hasler_Haddow_ET_AL_2012_Sparse_lexicalised_features_and_topic_adaptation_for_SMT.pdf

Sparse lexicalised features and topic adaptation for SMT

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