We compare different strategies to apply statistical machine translation techniques in order to retrieve documents which are a plausible translation of a given source document. Finding the translated version of a document is a relevant task, for example, when building a corpus of parallel texts that can help to create and to evaluate new machine translation systems. In contrast to the traditional settings in cross-language information retrieval tasks, in this case both the source and the target text are long and, thus, the procedure used to select what words or phrases will be included in the query has a key effect on the retrieval performance. In the statistical approach explored here, both the probability of the translation and the relevance of the terms are taken into account in order to build an effective query.
REF applied SMT technology to generate and translate queries in order to retrieve long documents.
Document translation retrieval based on statistical machine translation techniques
Applied Artificial Intelligence
Computer Science