Name
Abstract | ||
|---|---|---|
State-of-the-art Machine Translation (MT) systems are still far from being perfect. An alternative is the so-called Interactive Machine Translation (IMT) framework. In this framework, the knowledge of a human translator is combined with a MT system. The vast majority of the existing work on IMT makes use of the well-known batch learning paradigm. In the batch learning paradigm, the training of the IMT system and the interactive translation process are carried out in separate stages. This paradigm is not able to take advantage of the new knowledge produced by the user of the IMT system. In this paper, we present an application of the online learning paradigm to the IMT framework. In the online learning paradigm, the training and prediction stages are no longer separated. This feature is particularly useful in IMT since it allows the user feedback to be taken into account. The online learning techniques proposed here incrementally update the statistical models involved in the translation process. Empirical results show the great potential of online learning in the IMT framework. |
Year | Venue | Keywords |
|---|---|---|
2010 | HLT-NAACL | imt framework,interactive statistical machine translation,mt system,online learning,new knowledge,interactive translation process,so-called interactive machine translation,state-of-the-art machine translation,translation process,imt system,user feedback |
Field | DocType | ISBN |
Online learning,Online machine learning,Active learning (machine learning),Computer science,Machine translation,Interactive machine translation,Natural language processing,Artificial intelligence,Statistical model,Machine learning | Conference | 1-932432-65-5 |
Citations | PageRank | References |
31 | 1.12 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Ortiz-Martínez | 1 | 105 | 10.14 |
| Ismael García-varea | 2 | 275 | 36.16 |
| francisco casacuberta | 3 | 1439 | 161.33 |