Statistical Vs Rule Based Machine Translation; A Case Study on Indian Language Perspective

TL;DR

This study compares statistical and rule-based machine translation systems for English-Indian and Indian-Indian language pairs, showing SMT generally outperforms RBMT, especially with limited training data, in various translation scenarios.

Contribution

It provides a comprehensive five-way performance comparison and detailed analysis of SMT and RBMT systems for Indian languages, highlighting their relative strengths and weaknesses.

Findings

SMT outperforms RBMT in most scenarios

English-Indian SMT performs better than Indian-English SMT

SMT is more effective for Indian-Indian language translation

Abstract

In this paper we present our work on a case study between Statistical Machien Transaltion (SMT) and Rule-Based Machine Translation (RBMT) systems on English-Indian langugae and Indian to Indian langugae perspective. Main objective of our study is to make a five way performance compariosn; such as, a) SMT and RBMT b) SMT on English-Indian langugae c) RBMT on English-Indian langugae d) SMT on Indian to Indian langugae perspective e) RBMT on Indian to Indian langugae perspective. Through a detailed analysis we describe the Rule Based and the Statistical Machine Translation system developments and its evaluations. Through a detailed error analysis, we point out the relative strengths and weaknesses of both systems. The observations based on our study are: a) SMT systems outperforms RBMT b) In the case of SMT, English to Indian language MT systmes performs better than Indian to English langugae MT systems c) In the case of RBMT, English to Indian langugae MT systems perofrms better than Indian to Englsih Language MT systems d) SMT systems performs better for Indian to Indian language MT systems compared to RBMT. Effectively, we shall see that even with a small amount of training corpus a statistical machine translation system has many advantages for high quality domain specific machine translation over that of a rule-based counterpart.