2019/09/03 by Xuefeng Bai, Bai, Xuefeng, Yue Zhang +5
Computer Science · #Natural Language Processing Techniques #Topic Modeling #Multimodal Machine Learning Applications
paper · pdf · doi:10.48550/arxiv.1909.01013
Unsupervised bilingual lexicon induction naturally exhibits duality, which results from symmetry in back-translation. For example, EN-IT and IT-EN induction can be mutually primal and dual problems. Current state-of-the-art methods, however, consider the two tasks independently. In this paper, we propose to train primal and dual models jointly, using regularizers to encourage consistency in back translation cycles. Experiments across 6 language pairs show that the proposed method significantly outperforms competitive baselines, obtaining the best-published results on a standard benchmark.