We present an approach to mine comparable data for parallel sentences using translation-based cross-lingual information retrieval (CLIR). By iteratively alternating between the tasks of retrieval and translation, an initial general-domain model is allowed to adapt to in-domain data. Adaptation is done by training the translation system on a few thousand sentences retrieved in the step before. Our setup is time- and memory-efficient and of similar quality as CLIR-based adaptation on millions of parallel sentences.
['Felix Hieber', 'Laura Jehl', 'Stefan Riezler']
Task Alternation in Parallel Sentence Retrieval for Twitter Translation