Phrase Pair Rescoring with Term Weighting for Statistical Machine Translation
Abstract:
We propose to score phrase translation pairs for statistical machine translation using term weight based models. These models employ $tf.idf$ to encode the weights of content and non-content words in phrase translation pairs. The translation probability is then modeled by similarity functions defined in a vector space. Two similarity functions are compared. Using these models in a statistical machine translation task shows significant improvements.
| Zugehörige Institution(en) am KIT | Institut für Anthropomatik und Robotik (IAR) |
| Publikationstyp | Proceedingsbeitrag |
| Publikationsjahr | 2004 |
| Sprache | Englisch |
| Identifikator | KITopen-ID: 1000166438 |
| Erschienen in | Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. Ed.: D. Lin, D. Wu |
| Veranstaltung | Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), Barcelona, Spanien, 25.07.2004 – 26.07.2004 |
| Verlag | Association for Computational Linguistics (ACL) |
| Seiten | 206–213 |