Investigating the roles of sentiment in machine translation

Sainik Kumar Mahata, Dipankar Das, Sivaji Bandyopadhyay

Abstract: Parallel corpora are central to translation studies and contrastive linguistics. However, training machine translation (MT) systems by barely using the semantic aspects of a parallel corpus leads to unsatisfactory results, as then the trained MT systems are likely to generate target sentences that are semantically and pragmatically different from the source sentence. In the present work, we explore the improvement in the performance of an MT system when pragmatic features such as sentiment are introduced during its development. The language pair used for the experiments is English (source language) and Bengali (target language). The improvement in the MT output, before and after the introduction of sentiment features, is quantified by comparing various translation models, such as SMT, NMT and a newly developed translation model SeNA, with the help of automated (BLEU and TER) and manual evaluation metrics. In addition, the propagation of sentiment during the translation process is also studied extensively. We observe that the introduction of sentiment features during the system development process helps in elevating the translation quality.