Assessing and Improving Punctuation Robustness in English-Marathi Machine Translation
This addresses the issue of unreliable translations for users dealing with poorly punctuated text in low-resource language pairs, representing an incremental but domain-specific advancement.
The paper tackled the problem of punctuation robustness in English-Marathi neural machine translation by introducing a diagnostic benchmark and evaluating remediation strategies, resulting in substantial performance improvements, though large language models showed poorer robustness.
Neural Machine Translation (NMT) systems rely heavily on explicit punctuation cues to resolve semantic ambiguities in a source sentence. Inputting user-generated sentences, which are likely to contain missing or incorrect punctuation, results in fluent but semantically disastrous translations. This work attempts to highlight and address the problem of punctuation robustness of NMT systems through an English-to-Marathi translation. First, we introduce \textbf{\textit{Viram}}, a human-curated diagnostic benchmark of 54 punctuation-ambiguous English-Marathi sentence pairs to stress-test existing NMT systems. Second, we evaluate two simple remediation strategies: cascade-based \textit{restore-then-translate} and \textit{direct fine-tuning}. Our experimental results and analysis demonstrate that both strategies yield substantial NMT performance improvements. Furthermore, we find that current Large Language Models (LLMs) exhibit relatively poorer robustness in translating such sentences than these task-specific strategies, thus necessitating further research in this area. The code and dataset are available at https://github.com/KaustubhShejole/Viram_Marathi.