Summary of the paper
| Title | Using Reordering in Statistical Machine Translation based on Alignment Block Classification |
|---|---|
| Authors | Marta R. Costa-jussà, José A. R. Fonollosa and Enric Monte |
| Abstract | Statistical Machine Translation (SMT) is based on alignment models which learn from bilingual corpora the word correspondences between source and target language. These models are assumed to be capable of learning reorderings of sequences of words. However, the difference in word order between two languages is one of the most important sources of errors in SMT. This paper proposes a Recursive Alignment Block Classification algorithm (RABCA) that can take advantage of inductive learning in order to solve reordering problems. This algorithm should be able to cope with swapping examples seen during training; it should infer properties that might permit to reorder pairs of blocks (sequences of words) which did not appear during training; and finally it should be robust with respect to training errors and ambiguities. Experiments are reported on the EuroParl task and RABCA is tested using two state-of-the-art SMT systems: a phrased-based and an Ngram-based. In both cases, RABCA improves results. |
| Language | Multiple languages |
| Topics | Machine Translation, SpeechToSpeech Translation, Statistical methods, Other |
Full paper |
Using Reordering in Statistical Machine Translation based on Alignment Block Classification |
Slides |
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| Bibtex | @InProceedings{RCOSTAJUSS08.444, author = {Marta R. Costa-jussà, José A. R. Fonollosa and Enric Monte}, title = {Using Reordering in Statistical Machine Translation based on Alignment Block Classification}, booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)}, year = {2008}, month = {may}, date = {28-30}, address = {Marrakech, Morocco}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-4-0}, note = {http://www.lrec-conf.org/proceedings/lrec2008/}, language = {english} } |