A swarm-inspired re-ranker system for statistical machine translation

Recently, re-ranking algorithms have been successfully applied on statistical machine translation systems. Due to the errors in the hypothesis alignment and varying word order between the source and target sentences and also the lack of sufficient resources such as parallel corpora, decoding may result in ungrammatical or non-fluent outputs. This paper proposes a re-ranking system based on swarm algorithms, which makes the use of sophisticated non-syntactical features to re-rank the n-best translation candidates. We introduce plenty of easy-computed non-syntactical features to deal with SMT system errors plus the quantum-behaved particle swarm optimization (QPSO) algorithm to adjust the weights of features. We have evaluated the proposed approach on 2 translation tasks in different language pairs (Persian → English and German → English) and genres (news and novel books). In comparison with PSO-, GA-, Perceptron- and averaged Perceptron-style re-ranking systems, the experimental study demonstrates the superiority of the proposed system in terms of translation quality on both translation tasks. In addition, the impacts of the proposed features on the translation quality have been analyzed, and the most positive ones have been recognized. At the end, the impact of the n-best list size on the proposed system is investigated.