Urdu-English Machine Transliteration using Neural Networks

TL;DR

This paper introduces an unsupervised, language-independent transliteration method for Urdu-English translation using Expectation Maximization, improving handling of OOV words without requiring large transliteration datasets.

Contribution

It presents a novel EM-based transliteration technique applicable to low-resource languages, integrated with multiple translation models, without needing explicit transliteration training data.

Findings

Effective handling of OOV words in low-resource language translation

Improved transliteration accuracy across different translation models

Unsupervised approach reduces need for large transliteration datasets

Abstract

Machine translation has gained much attention in recent years. It is a sub-field of computational linguistic which focus on translating text from one language to other language. Among different translation techniques, neural network currently leading the domain with its capabilities of providing a single large neural network with attention mechanism, sequence-to-sequence and long-short term modelling. Despite significant progress in domain of machine translation, translation of out-of-vocabulary words(OOV) which include technical terms, named-entities, foreign words are still a challenge for current state-of-art translation systems, and this situation becomes even worse while translating between low resource languages or languages having different structures. Due to morphological richness of a language, a word may have different meninges in different context. In such scenarios, translation of word is not only enough in order provide the correct/quality translation. Transliteration is a way to consider the context of word/sentence during translation. For low resource language like Urdu, it is very difficult to have/find parallel corpus for transliteration which is large enough to train the system. In this work, we presented transliteration technique based on Expectation Maximization (EM) which is un-supervised and language independent. Systems learns the pattern and out-of-vocabulary (OOV) words from parallel corpus and there is no need to train it on transliteration corpus explicitly. This approach is tested on three models of statistical machine translation (SMT) which include phrasebased, hierarchical phrase-based and factor based models and two models of neural machine translation which include LSTM and transformer model.