Name
Abstract | ||
|---|---|---|
Statistical machine translation is often faced with the problem of combining training data from many diverse sources into a single translation model which then has to translate sentences in a new domain. We propose a novel approach, ensemble decoding, which combines a number of translation systems dynamically at the decoding step. In this paper, we evaluate performance on a domain adaptation setting where we translate sentences from the medical domain. Our experimental results show that ensemble decoding outperforms various strong baselines including mixture models, the current state-of-the-art for domain adaptation in machine translation. |
Year | Venue | Keywords |
|---|---|---|
2012 | ACL | statistical machine translation,new domain,decoding step,translation systems dynamically,ensemble decoding,medical domain,machine translation,multiple translation model,diverse source,single translation model,domain adaptation |
Field | DocType | Volume |
Rule-based machine translation,Example-based machine translation,Domain adaptation,Computer science,Machine translation,Synchronous context-free grammar,Natural language processing,Artificial intelligence,Speech recognition,Transfer-based machine translation,Decoding methods,Mixture model,Machine learning | Conference | P12-1 |
Citations | PageRank | References |
16 | 0.64 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Majid Razmara | 1 | 94 | 8.49 |
| George F. Foster | 2 | 570 | 47.75 |
| Baskaran Sankaran | 3 | 155 | 13.65 |
| Anoop Sarkar | 4 | 1017 | 88.82 |