Xun Yang, Yunshan Ma, Lizi Liao, Meng Wang, Tat-Seng Chua TransNFCM: Translation-Based Neural Fashion Compatibility Modeling иї”е›ћз›®еЅ• Experiments conducted on en-fr and en-de translation tasks show that our method outperforms the strong baseline and achieves new state-of-the-art unsupervised machine translation performance.Д¤. In this way, (1) the negative effect caused by errors in the iterative back-translation process can be alleviated timely by SMT filtering noises from its phrase tables meanwhile, (2) NMT can compensate for the deficiency of fluency inherent in SMT. Then SMT and NMT models are optimized jointly and boost each other incrementally in a unified EM framework. Our method starts from SMT models built with pre-trained language models and word-level translation tables inferred from cross-lingual embeddings. To address this issue, we introduce phrase based Statistic Machine Translation (SMT) models which are robust to noisy data, as posterior regularizations to guide the training of unsupervised NMT models in the iterative back-translation process. However, due to weak supervision, the pseudo data inevitably contain noises and errors that will be accumulated and reinforced in the subsequent training process, leading to bad translation performance. Without real bilingual corpus available, unsupervised Neural Machine Translation (NMT) typically requires pseudo parallel data generated with the back-translation method for the model training. Shuo Ren, Zhirui Zhang, Shujie Liu, Ming Zhou, Shuai Ma Unsupervised Neural Machine Translation with SMT as Posterior Regularization иї”е›ћз›®еЅ• On WMT14 Englishв‡’German and Englishв‡’French translation tasks, DTMT shows superior quality to the state-of-the-art NMT systems, including the Transformer and the RNMT+.ДЈ. Experimental results on Chineseв‡’English translation task show that DTMT can outperform the Transformer model by +2.09 BLEU points and achieve the best results ever reported in the same dataset. Experiments show that with the specially designed deep transition modules, our DTMT can achieve remarkable improvements on translation quality. This model enhances the hidden-to-hidden transition with multiple non-linear transformations, as well as maintains a linear transformation path throughout this deep transition by the well-designed linear transformation mechanism to alleviate the gradient vanishing problem. In this paper, we further enhance the RNN-based NMT through increasing the transition depth between consecutive hidden states and build a novel Deep Transition RNN-based Architecture for Neural Machine Translation, named DTMT. Although the RNMT model can possess very deep architectures through stacking layers, the transition depth between consecutive hidden states along the sequential axis is still shallow. Most recently, with advanced modeling and training techniques, the RNN-based NMT (RNMT) has shown its potential strength, even compared with the well-known Transformer (self-attentional) model. Past years have witnessed rapid developments in Neural Machine Translation (NMT). DTMT: A Novel Deep Transition Architecture for Neural Machine Translation иї”е›ћз›®еЅ• Experimental results across language pairs show that the proposed approach consistently outperforms the strong baseline model and a representative static aggregation model.Дў. We implement our algorithm on top of the state-of-the-art neural machine translation model TRANSFORMER and conduct experiments on the widely-used WMT14 shв‡’German and WMT17 Chineseв‡’English translation datasets. Specifically, the algorithm learns the probability of a part (individual layer representations) assigned to a whole (aggregated representations) in an iterative way and combines parts accordingly. Inspired by recent progress on capsule networks, in this paper we propose to use routing-by-agreement strategies to aggregate layers dynamically.

However, most of the previous methods combine layers in a static fashion in that their aggregation strategy is independent of specific hidden states. With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine translation. Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Longyue Wang, Shuming Shi, Tong Zhang Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement иї”е›ћз›®еЅ•ДЄAAI 2019.

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