An Error-based Investigation of Statistical and Neural Machine Translation Performance on Hindi-to-Tamil and English-to-Tamil
Akshai Ramesh, Venkatesh Balavadhani parthasa, Rejwanul Haque, Andy Way · 2020
Statistical machine translation (SMT) was the state-of-the-art in machine translation (MT) research for more than two decades, but has since been superseded by neural MT (NMT).Despite producing state-of-the-art results in many translation tasks, neural models underperform in resource-poor scenarios.Despite some success, none of the present-day benchmarks that have tried to overcome this problem can be regarded as a universal solution to the problem of translation of many low-resource languages.In this work, we investigate the performance of phrasebased SMT (PB-SMT) and NMT on two rarelytested low-resource language-pairs, English-to-Tamil and Hindi-to-Tamil, taking a specialised data domain (software localisation) into consideration.This paper demonstrates our findings including the identification of several issues of the current neural approaches to low-resource domain-specific text translation.