Name
Title | ||
|---|---|---|
Adaptive model weighting and transductive regression for predicting best system combinations |
Abstract | ||
|---|---|---|
We analyze adaptive model weighting techniques for reranking using instance scores obtained by L1 regularized transductive regression. Competitive statistical machine translation is an on-line learning technique for sequential translation tasks where we try to select the best among competing statistical machine translators. The competitive predictor assigns a probability per model weighted by the sequential performance. We define additive, multiplicative, and loss-based weight updates with exponential loss functions for competitive statistical machine translation. Without any pre-knowledge of the performance of the translation models, we succeed in achieving the performance of the best model in all systems and surpass their performance in most of the language pairs we considered. |
Year | Venue | Keywords |
|---|---|---|
2010 | WMT@ACL | best model,statistical machine translator,sequential performance,best system combination,sequential translation task,translation model,exponential loss function,l1 regularized transductive regression,competitive predictor,adaptive model weighting technique,competitive statistical machine translation,machine translation,loss function |
Field | DocType | Citations |
Transduction (machine learning),Weighting,Exponential function,Multiplicative function,Regression,Computer science,Machine translation,Artificial intelligence,Machine learning | Conference | 2 |
PageRank | References | Authors |
0.49 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ergun Biçici | 1 | 133 | 13.23 |
| S. Serdar Kozat | 2 | 2 | 0.49 |