Statistical-based system combination approach to gain advantages over different machine translation systems

• Main Authors: Debajyoty Banik, Asif Ekbal, Pushpak Bhattacharyya, Siddhartha Bhattacharyya, Jan Platos
• Format: Article
• Language: English
• Published: Elsevier 2019-09-01
• Series: Heliyon
• Subjects: System combination method; Machine translation; Statistical approach; Neural machine translation (NMT); Neural network; Hierarchical machine translation (Hiero) systems
• Online Access: http://www.sciencedirect.com/science/article/pii/S240584401936164X

Every machine translation system has some advantages. We propose an improved statistical system combination approach to achieve the advantages of existing machine translation systems. The primary task is to score all the phrases of the outputs of different machine translation systems selected for combination. Three steps are involved in the proposed statistical system combination approach, viz., alignment, decoding, and scoring. Pair alignment is done in the first step to prevent duplication so that only a single phrase is chosen from various phrases containing the same information. Thus the alignment and scoring strategy are implemented in our approach. Hypotheses are built in the second step. In the third step, we calculate the scores for all the hypotheses. The hypothesis with the highest score is chosen as the final translated output. Wrong scoring can mislead to identify the best part from different systems. It may be noted that a particular phrase may appear in various ways in different translations. To resolve the challenges, we incorporate WordNet in the alignment phase and word2vec in the scoring phase along with the existing factors. We find that the system combination model using WordNet and word2vec injection improves the machine translation accuracy. In this work, we have merged three systems viz., Hierarchical machine translation system, Bing Microsoft Translate, and Google Translate. The broad tests of translation on eight language pairs with benchmark datasets demonstrate that the proposed system achieves better quality than the individual systems and the state-of-the-art system combination models.