Domain Transfer based Data Augmentation for Neural Query Translation
Liang Yao, Baosong Yang, Haibo Zhang, Boxing Chen, Weihua Luo · 2020
Query translation (QT) serves as a critical factor in successful cross-lingual information retrieval (CLIR).Due to the lack of parallel query samples, neural-based QT models are usually optimized with synthetic data which are derived from large-scale monolingual queries.Nevertheless, such kind of pseudo corpus is mostly produced by a general-domain translation model, making it be insufficient to guide the learning of QT model.In this paper, we extend the data augmentation with a domain transfer procedure, thus to revise synthetic candidates to search-aware examples.Specifically, the domain transfer model is built upon advanced Transformer, in which layer coordination and mixed attention are exploited to speed up the refining process and leverage parameters from a pre-trained cross-lingual language model.In order to examine the effectiveness of the proposed method, we collected French-to-English and Spanish-to-English QT test sets, each of which consists of 10,000 parallel query pairs with careful manual-checking.Qualitative and quantitative analyses reveal that our model significantly outperforms strong baselines and the related domain transfer methods on both translation quality and retrieval accuracy.1