Name
Abstract | ||
|---|---|---|
This paper presents efficient algorithms for expected similarity maximization, which co- incides with minimum Bayes decoding for a similarity-based loss function. Our algorithms are designed for similarity functions that are sequence kernels in a general class of posi- tive definite symmetric kernels. We discuss both a general algorithm and a more efficient algorithm applicable in a common unambigu- ous scenario. We also describe the applica- tion of our algorithms to machine translation and report the results of experiments with sev- eral translation data sets which demonstrate a substantial speed-up. In particular, our results show a speed-up by two orders of magnitude with respect to the original method of Tromble et al. (2008) and by a factor of 3 or more even with respect to an approximate algorithm specifically designed for that task. These re- sults open the path for the exploration of more appropriate or optimal kernels for the specific tasks considered. |
Year | Venue | Keywords |
|---|---|---|
2010 | North American Chapter of the Association for Computational Linguistics | translation data set,expected sequence similarity maximization,similarity function,common unambiguous scenario,general algorithm,expected similarity maximization,substantial speed-up,machine translation,general class,efficient algorithm,approximate algorithm,generic algorithm,loss function |
Field | DocType | ISBN |
Data set,General algorithm,Computer science,Positive-definite matrix,Machine translation,Artificial intelligence,Decoding methods,Order of magnitude,Maximization,Machine learning,Bayes' theorem | Conference | 1-932432-65-5 |
Citations | PageRank | References |
2 | 0.39 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cyril Allauzen | 1 | 690 | 47.64 |
| Shankar Kumar | 2 | 131 | 6.04 |
| Wolfgang Macherey | 3 | 617 | 45.06 |
| Mehryar Mohri | 4 | 4502 | 448.21 |
| Michael Riley | 5 | 102 | 7.13 |