Computational linguistics deals with the computational modelling of natural languages, in which machine translation is a popular task. The aim of machine translation is to automatically translate one natural language into another, which minimizes the linguistic barrier of different linguistic backgrounds. The datadrivenapproach of machine translation, namely, neural machine translation achieves state-of-the-art results on different language pairs, however it needs a sufficient amount of parallel training data to attain reasonable translation performance. In this work, we have explored different machine translation models on a low-resource English–Assamese language pair and investigated different sources of errors, particularly due to negation in English-to-Assamese and Assamese-to-English translation. Negation is a universal, essential feature of humanlanguage that has a substantial impact on the semantics of a statement. Moreover, a rule-based approach is proposed in the data preprocessing step which handles modal-verb negation problem that shows significant improvement in translation performance in terms of automatic and manual evaluation scores.