SemMT: A Semantic-Based Testing Approach for Machine Translation Systems

Abstract

Machine translation has wide applications in daily life. In mission-critical applications such as translating official documents, incorrect translation can have unpleasant or sometimes catastrophic consequences. This motivates recent research on the testing methodologies for machine translation systems. Existing methodologies mostly rely on metamorphic relations designed at the textual level (e.g., Levenshtein distance) or syntactic level (e.g., distance between grammar structures) to determine the correctness of translation results. However, these metamorphic relations do not consider whether the original and the translated sentences have the same meaning (i.e., semantic similarity). To address this problem, in this article we propose SemMT, an automatic testing approach for machine translation systems based on semantic similarity checking. SemMT applies round-trip translation and measures the semantic similarity between the original and the translated sentences. Our insight is that the semantics concerning logical relations and quantifiers in sentences can be captured by regular expressions (or deterministic finite automata) where efficient semantic equivalence/similarity checking algorithms can be applied. Leveraging the insight, we propose three semantic similarity metrics and implement them in SemMT. We compared SemMT with related state-of-the-art testing techniques, demonstrating the effectiveness of mistranslation detection. The experiment results show that SemMT outperforms existing metrics, achieving an increase of 34.2% and 15.4% on accuracy and F-score, respectively. We also study the possibility of further enhancing the performance by combining various metrics. Finally, we discuss a solution to locate the suspicious trip in round-trip translation, which provides hints for bug diagnosis.