@inproceedings{hasler-etal-2012-sparse,
    title = "Sparse lexicalised features and topic adaptation for {SMT}",
    author = "Hasler, Eva  and
      Haddow, Barry  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 9th International Workshop on Spoken Language Translation: Papers",
    month = dec # " 6-7",
    year = "2012",
    address = "Hong Kong, Table of contents",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2012.iwslt-papers.17/",
    pages = "268--275",
    abstract = "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."
}