Name
Abstract | ||
|---|---|---|
The language model (LM) is a critical component in most statistical machine translation (SMT) systems, serving to establish a probability distribution over the hypothesis space. Most SMT systems use a static LM, independent of the source language input. While previous work has shown that adapting LMs based on the input improves SMT performance, none of the techniques has thus far been shown to be feasible for on-line systems. In this paper, we develop a novel measure of cross-lingual similarity for biasing the LM based on the test input. We also illustrate an efficient on-line implementation that supports integration with on-line SMT systems by transferring much of the computational load off-line. Our approach yields significant reductions in target perplexity compared to the static LM, as well as consistent improvements in SMT performance across language pairs (English-Dari and English-Pashto). |
Year | Venue | Keywords |
|---|---|---|
2011 | ACL (Short Papers) | statistical machine translation,source language input,test input,language model,on-line smt system,on-line language model,language pair,smt system,static lm,smt performance,efficient on-line implementation,on-line system |
Field | DocType | Volume |
Perplexity,Computer science,Machine translation,Speech recognition,Probability distribution,Natural language processing,Artificial intelligence,Language model,Biasing | Conference | P11-2 |
Citations | PageRank | References |
5 | 0.42 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sankaranarayanan Ananthakrishnan | 1 | 134 | 13.29 |
| Rohit Prasad | 2 | 465 | 39.06 |
| Premkumar Natarajan | 3 | 874 | 79.46 |
| raytheon bbn | 4 | 141 | 5.38 |