Mitigating Problems in Analogy-based EBMT with SMT and vice versa: A Case Study with Named Entity Transliteration
Sandipan Dandapat, Sara Morrissey, Sudip Kumar Naskar, HAROLD L. SOMERS · Institutional Repositories DataBase (IRDB) · 2010
Five years ago, a number of papers reported an experimental implementation of an Example Based Machine Translation (EBMT) system using proportional analogy.This approach, a type of analogical learning, was attractive because of its simplicity; and the paper reported considerable success with the method using various language pairs.In this paper, we describe our attempt to use this approach for tackling English-Hindi Named Entity (NE) Transliteration.We have implemented our own EBMT system using proportional analogy and have found that the analogy-based system on its own has low precision but a high recall due to the fact that a large number of names are untransliterated with the approach.However, mitigating problems in analogy-based EBMT with SMT and vice-versa have shown considerable improvement over the individual approach.