Chenglong Wang


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The NiuTrans Machine Translation Systems for WMT21
Shuhan Zhou | Tao Zhou | Binghao Wei | Yingfeng Luo | Yongyu Mu | Zefan Zhou | Chenglong Wang | Xuanjun Zhou | Chuanhao Lv | Yi Jing | Laohu Wang | Jingnan Zhang | Canan Huang | Zhongxiang Yan | Chi Hu | Bei Li | Tong Xiao | Jingbo Zhu
Proceedings of the Sixth Conference on Machine Translation

This paper describes NiuTrans neural machine translation systems of the WMT 2021 news translation tasks. We made submissions to 9 language directions, including English2Chinese, Japanese, Russian, Icelandic and English2Hausa tasks. Our primary systems are built on several effective variants of Transformer, e.g., Transformer-DLCL, ODE-Transformer. We also utilize back-translation, knowledge distillation, post-ensemble, and iterative fine-tuning techniques to enhance the model performance further.

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The NiuTrans System for the WMT 2021 Efficiency Task
Chenglong Wang | Chi Hu | Yongyu Mu | Zhongxiang Yan | Siming Wu | Yimin Hu | Hang Cao | Bei Li | Ye Lin | Tong Xiao | Jingbo Zhu
Proceedings of the Sixth Conference on Machine Translation

This paper describes the NiuTrans system for the WMT21 translation efficiency task. Following last year’s work, we explore various techniques to improve the efficiency while maintaining translation quality. We investigate the combinations of lightweight Transformer architectures and knowledge distillation strategies. Also, we improve the translation efficiency with graph optimization, low precision, dynamic batching, and parallel pre/post-processing. Putting these together, our system can translate 247,000 words per second on an NVIDIA A100, being 3× faster than our last year’s system. Our system is the fastest and has the lowest memory consumption on the GPU-throughput track. The code, model, and pipeline will be available at NiuTrans.NMT.

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RankNAS: Efficient Neural Architecture Search by Pairwise Ranking
Chi Hu | Chenglong Wang | Xiangnan Ma | Xia Meng | Yinqiao Li | Tong Xiao | Jingbo Zhu | Changliang Li
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

This paper addresses the efficiency challenge of Neural Architecture Search (NAS) by formulating the task as a ranking problem. Previous methods require numerous training examples to estimate the accurate performance of architectures, although the actual goal is to find the distinction between “good” and “bad” candidates. Here we do not resort to performance predictors. Instead, we propose a performance ranking method (RankNAS) via pairwise ranking. It enables efficient architecture search using much fewer training examples. Moreover, we develop an architecture selection method to prune the search space and concentrate on more promising candidates. Extensive experiments on machine translation and language modeling tasks show that RankNAS can design high-performance architectures while being orders of magnitude faster than state-of-the-art NAS systems.


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The NiuTrans System for WNGT 2020 Efficiency Task
Chi Hu | Bei Li | Yinqiao Li | Ye Lin | Yanyang Li | Chenglong Wang | Tong Xiao | Jingbo Zhu
Proceedings of the Fourth Workshop on Neural Generation and Translation

This paper describes the submissions of the NiuTrans Team to the WNGT 2020 Efficiency Shared Task. We focus on the efficient implementation of deep Transformer models (Wang et al., 2019; Li et al., 2019) using NiuTensor, a flexible toolkit for NLP tasks. We explored the combination of deep encoder and shallow decoder in Transformer models via model compression and knowledge distillation. The neural machine translation decoding also benefits from FP16 inference, attention caching, dynamic batching, and batch pruning. Our systems achieve promising results in both translation quality and efficiency, e.g., our fastest system can translate more than 40,000 tokens per second with an RTX 2080 Ti while maintaining 42.9 BLEU on newstest2018.

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The NiuTrans System for the WMT20 Quality Estimation Shared Task
Chi Hu | Hui Liu | Kai Feng | Chen Xu | Nuo Xu | Zefan Zhou | Shiqin Yan | Yingfeng Luo | Chenglong Wang | Xia Meng | Tong Xiao | Jingbo Zhu
Proceedings of the Fifth Conference on Machine Translation

This paper describes the submissions of the NiuTrans Team to the WMT 2020 Quality Estimation Shared Task. We participated in all tasks and all language pairs. We explored the combination of transfer learning, multi-task learning and model ensemble. Results on multiple tasks show that deep transformer machine translation models and multilingual pretraining methods significantly improve translation quality estimation performance. Our system achieved remarkable results in multiple level tasks, e.g., our submissions obtained the best results on all tracks in the sentence-level Direct Assessment task.


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NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System
Xi Victoria Lin | Chenglong Wang | Luke Zettlemoyer | Michael D. Ernst
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Natural Language to Structured Query Generation via Meta-Learning
Po-Sen Huang | Chenglong Wang | Rishabh Singh | Wen-tau Yih | Xiaodong He
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

In conventional supervised training, a model is trained to fit all the training examples. However, having a monolithic model may not always be the best strategy, as examples could vary widely. In this work, we explore a different learning protocol that treats each example as a unique pseudo-task, by reducing the original learning problem to a few-shot meta-learning scenario with the help of a domain-dependent relevance function. When evaluated on the WikiSQL dataset, our approach leads to faster convergence and achieves 1.1%–5.4% absolute accuracy gains over the non-meta-learning counterparts.