New Confidence Measures for Statistical Machine Translation
| dc.creator | Raybaud, Sylvain | |
| dc.creator | Lavecchia, Caroline | |
| dc.creator | Langlois, David | |
| dc.creator | Smaïli, Kamel | |
| dc.date | 2009-02-06 | |
| dc.date.accessioned | 2026-07-07T12:38:53Z | |
| dc.date.available | 2026-07-07T12:38:53Z | |
| dc.description | A confidence measure is able to estimate the reliability of an hypothesis provided by a machine translation system. The problem of confidence measure can be seen as a process of testing : we want to decide whether the most probable sequence of words provided by the machine translation system is correct or not. In the following we describe several original word-level confidence measures for machine translation, based on mutual information, n-gram language model and lexical features language model. We evaluate how well they perform individually or together, and show that using a combination of confidence measures based on mutual information yields a classification error rate as low as 25.1% with an F-measure of 0.708. | |
| dc.identifier | https://arxiv.org/abs/0902.1033 | |
| dc.identifier | http://arxiv.org/abs/0902.1033 | |
| dc.identifier | International Conference On Agents and Artificial Intelligence - ICAART 09 (2009) | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/218921 | |
| dc.subject | Computation and Language | |
| dc.title | New Confidence Measures for Statistical Machine Translation | |
| dc.type | text |