This systematic review paper provides an overview of recent machine translation and transliteration developments for Indo-Aryan languages spoken by a large population across South Asia. The paper examines advancements in translation and transliteration systems for a few language pairs which appear in recently published papers. The review summarizes the current state of these technologies, providing a worthful resource for anyone who is doing research in these fields to understand and find existing systems and techniques for translation and transliteration.

The Romanized text has become popular with the growth of digital communication platforms, largely due to the familiarity with English keyboards. In Sri Lanka, Romanized Sinhala, commonly referred to as “Singlish” is widely used in digital communications. This paper introduces a novel context-aware back-transliteration system designed to address the ad-hoc typing patterns and lexical ambiguity inherent in Singlish. The proposed system com bines dictionary-based mapping for Singlish words, a rule-based transliteration for out of-vocabulary words and a BERT-based language model for addressing lexical ambiguities. Evaluation results demonstrate the robustness of the proposed approach, achieving high BLEU scores along with low Word Error Rate (WER) and Character Error Rate (CER) across test datasets. This study provides an effective solution for Romanized Sinhala back-transliteration and establishes the foundation for improving NLP tools for similar low-resourced languages.

Ambiguous words are often found within modern digital communications. Lexical ambiguity challenges traditional Word Sense Disambiguation (WSD) methods, due to limited data. Consequently, the efficiency of translation, information retrieval, and question-answering systems is hindered by these limitations. This study investigates the use of Large Language Models (LLMs) to improve WSD using a novel approach combining a systematic prompt augmentation mechanism with a knowledge base (KB) consisting of different sense interpretations. The proposed method incorporates a human-in-loop approach for prompt augmentation where prompt is supported by Part-of-Speech (POS) tagging, synonyms of ambiguous words, aspect-based sense filtering and few-shot prompting to guide the LLM. By utilizing a few-shot Chain of Thought (COT) prompting-based approach, this work demonstrates a substantial improvement in performance. The evaluation was conducted using FEWS test data and sense tags. This research advances accurate word interpretation in social media and digital communication.