  Zero-shot translation, directly translating between language pairs unseen in
training, is a promising capability of multilingual neural machine translation
(NMT). However, it usually suffers from capturing spurious correlations between
the output language and language invariant semantics due to the maximum
likelihood training objective, leading to poor transfer performance on
zero-shot translation. In this paper, we introduce a denoising autoencoder
objective based on pivot language into traditional training objective to
improve the translation accuracy on zero-shot directions. The theoretical
analysis from the perspective of latent variables shows that our approach
actually implicitly maximizes the probability distributions for zero-shot
directions. On two benchmark machine translation datasets, we demonstrate that
the proposed method is able to effectively eliminate the spurious correlations
and significantly outperforms state-of-the-art methods with a remarkable
performance. Our code is available at https://github.com/Victorwz/zs-nmt-dae.
