Qiang Wang


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Layer-Wise Multi-View Learning for Neural Machine Translation
Qiang Wang | Changliang Li | Yue Zhang | Tong Xiao | Jingbo Zhu
Proceedings of the 28th International Conference on Computational Linguistics

Traditional neural machine translation is limited to the topmost encoder layer’s context representation and cannot directly perceive the lower encoder layers. Existing solutions usually rely on the adjustment of network architecture, making the calculation more complicated or introducing additional structural restrictions. In this work, we propose layer-wise multi-view learning to solve this problem, circumventing the necessity to change the model structure. We regard each encoder layer’s off-the-shelf output, a by-product in layer-by-layer encoding, as the redundant view for the input sentence. In this way, in addition to the topmost encoder layer (referred to as the primary view), we also incorporate an intermediate encoder layer as the auxiliary view. We feed the two views to a partially shared decoder to maintain independent prediction. Consistency regularization based on KL divergence is used to encourage the two views to learn from each other. Extensive experimental results on five translation tasks show that our approach yields stable improvements over multiple strong baselines. As another bonus, our method is agnostic to network architectures and can maintain the same inference speed as the original model.

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Training Flexible Depth Model by Multi-Task Learning for Neural Machine Translation
Qiang Wang | Tong Xiao | Jingbo Zhu
Findings of the Association for Computational Linguistics: EMNLP 2020

The standard neural machine translation model can only decode with the same depth configuration as training. Restricted by this feature, we have to deploy models of various sizes to maintain the same translation latency, because the hardware conditions on different terminal devices (e.g., mobile phones) may vary greatly. Such individual training leads to increased model maintenance costs and slower model iterations, especially for the industry. In this work, we propose to use multi-task learning to train a flexible depth model that can adapt to different depth configurations during inference. Experimental results show that our approach can simultaneously support decoding in 24 depth configurations and is superior to the individual training and another flexible depth model training method——LayerDrop.


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The LAIX Systems in the BEA-2019 GEC Shared Task
Ruobing Li | Chuan Wang | Yefei Zha | Yonghong Yu | Shiman Guo | Qiang Wang | Yang Liu | Hui Lin
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

In this paper, we describe two systems we developed for the three tracks we have participated in the BEA-2019 GEC Shared Task. We investigate competitive classification models with bi-directional recurrent neural networks (Bi-RNN) and neural machine translation (NMT) models. For different tracks, we use ensemble systems to selectively combine the NMT models, the classification models, and some rules, and demonstrate that an ensemble solution can effectively improve GEC performance over single systems. Our GEC systems ranked the first in the Unrestricted Track, and the third in both the Restricted Track and the Low Resource Track.

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The NiuTrans Machine Translation Systems for WMT19
Bei Li | Yinqiao Li | Chen Xu | Ye Lin | Jiqiang Liu | Hui Liu | Ziyang Wang | Yuhao Zhang | Nuo Xu | Zeyang Wang | Kai Feng | Hexuan Chen | Tengbo Liu | Yanyang Li | Qiang Wang | Tong Xiao | Jingbo Zhu
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper described NiuTrans neural machine translation systems for the WMT 2019 news translation tasks. We participated in 13 translation directions, including 11 supervised tasks, namely EN↔{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE↔CS sub-track. Our systems were built on Deep Transformer and several back-translation methods. Iterative knowledge distillation and ensemble+reranking were also employed to obtain stronger models. Our unsupervised submissions were based on NMT enhanced by SMT. As a result, we achieved the highest BLEU scores in {KK↔EN, GU→EN} directions, ranking 2nd in {RU→EN, DE↔CS} and 3rd in {ZH→EN, LT→EN, EN→RU, EN↔DE} among all constrained submissions.

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Learning Deep Transformer Models for Machine Translation
Qiang Wang | Bei Li | Tong Xiao | Jingbo Zhu | Changliang Li | Derek F. Wong | Lidia S. Chao
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Transformer is the state-of-the-art model in recent machine translation evaluations. Two strands of research are promising to improve models of this kind: the first uses wide networks (a.k.a. Transformer-Big) and has been the de facto standard for development of the Transformer system, and the other uses deeper language representation but faces the difficulty arising from learning deep networks. Here, we continue the line of research on the latter. We claim that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a novel way of passing the combination of previous layers to the next. On WMT’16 English-German and NIST OpenMT’12 Chinese-English tasks, our deep system (30/25-layer encoder) outperforms the shallow Transformer-Big/Base baseline (6-layer encoder) by 0.4-2.4 BLEU points. As another bonus, the deep model is 1.6X smaller in size and 3X faster in training than Transformer-Big.


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The NiuTrans Machine Translation System for WMT18
Qiang Wang | Bei Li | Jiqiang Liu | Bojian Jiang | Zheyang Zhang | Yinqiao Li | Ye Lin | Tong Xiao | Jingbo Zhu
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the submission of the NiuTrans neural machine translation system for the WMT 2018 Chinese ↔ English news translation tasks. Our baseline systems are based on the Transformer architecture. We further improve the translation performance 2.4-2.6 BLEU points from four aspects, including architectural improvements, diverse ensemble decoding, reranking, and post-processing. Among constrained submissions, we rank 2nd out of 16 submitted systems on Chinese → English task and 3rd out of 16 on English → Chinese task, respectively.

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A Simple and Effective Approach to Coverage-Aware Neural Machine Translation
Yanyang Li | Tong Xiao | Yinqiao Li | Qiang Wang | Changming Xu | Jingbo Zhu
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We offer a simple and effective method to seek a better balance between model confidence and length preference for Neural Machine Translation (NMT). Unlike the popular length normalization and coverage models, our model does not require training nor reranking the limited n-best outputs. Moreover, it is robust to large beam sizes, which is not well studied in previous work. On the Chinese-English and English-German translation tasks, our approach yields +0.4 1.5 BLEU improvements over the state-of-the-art baselines.

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Multi-layer Representation Fusion for Neural Machine Translation
Qiang Wang | Fuxue Li | Tong Xiao | Yanyang Li | Yinqiao Li | Jingbo Zhu
Proceedings of the 27th International Conference on Computational Linguistics

Neural machine translation systems require a number of stacked layers for deep models. But the prediction depends on the sentence representation of the top-most layer with no access to low-level representations. This makes it more difficult to train the model and poses a risk of information loss to prediction. In this paper, we propose a multi-layer representation fusion (MLRF) approach to fusing stacked layers. In particular, we design three fusion functions to learn a better representation from the stack. Experimental results show that our approach yields improvements of 0.92 and 0.56 BLEU points over the strong Transformer baseline on IWSLT German-English and NIST Chinese-English MT tasks respectively. The result is new state-of-the-art in German-English translation.


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NiuParser: A Chinese Syntactic and Semantic Parsing Toolkit
Jingbo Zhu | Muhua Zhu | Qiang Wang | Tong Xiao
Proceedings of ACL-IJCNLP 2015 System Demonstrations