Yong Wang


pdf bib
ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning
Xin Xie | Xiangnan Chen | Xiang Chen | Yong Wang | Ningyu Zhang | Shumin Deng | Huajun Chen
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model. Inspired by the similarity of the ReCAM task and the language pre-training, we propose a simple yet effective technology, namely, negative augmentation with language model. Evaluation results demonstrate the effectiveness of our proposed approach. Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and an accuracy of 92.8%, respectively. We further conduct comprehensive model analysis and observe interesting error cases, which may promote future researches. The code and dataset used in our paper can be found at https://github.com/CheaSim/SemEval2021. The leaderboard can be found at https://competitions.codalab.org/competitions/26153.


pdf bib
On the Sparsity of Neural Machine Translation Models
Yong Wang | Longyue Wang | Victor Li | Zhaopeng Tu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Modern neural machine translation (NMT) models employ a large number of parameters, which leads to serious over-parameterization and typically causes the underutilization of computational resources. In response to this problem, we empirically investigate whether the redundant parameters can be reused to achieve better performance. Experiments and analyses are systematically conducted on different datasets and NMT architectures. We show that: 1) the pruned parameters can be rejuvenated to improve the baseline model by up to +0.8 BLEU points; 2) the rejuvenated parameters are reallocated to enhance the ability of modeling low-level lexical information.


pdf bib
Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations
Jiatao Gu | Yong Wang | Kyunghyun Cho | Victor O.K. Li
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Zero-shot translation, translating between language pairs on which a Neural Machine Translation (NMT) system has never been trained, is an emergent property when training the system in multilingual settings. However, naive training for zero-shot NMT easily fails, and is sensitive to hyper-parameter setting. The performance typically lags far behind the more conventional pivot-based approach which translates twice using a third language as a pivot. In this work, we address the degeneracy problem due to capturing spurious correlations by quantitatively analyzing the mutual information between language IDs of the source and decoded sentences. Inspired by this analysis, we propose to use two simple but effective approaches: (1) decoder pre-training; (2) back-translation. These methods show significant improvement (4 22 BLEU points) over the vanilla zero-shot translation on three challenging multilingual datasets, and achieve similar or better results than the pivot-based approach.


pdf bib
Meta-Learning for Low-Resource Neural Machine Translation
Jiatao Gu | Yong Wang | Yun Chen | Victor O. K. Li | Kyunghyun Cho
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML, Finn, et al., 2017) for low-resource neural machine translation (NMT). We frame low-resource translation as a meta-learning problem where we learn to adapt to low-resource languages based on multilingual high-resource language tasks. We use the universal lexical representation (Gu et al., 2018b) to overcome the input-output mismatch across different languages. We evaluate the proposed meta-learning strategy using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt, Nl, Pl, Pt, Sk, Sl, Sv and Ru) as source tasks and five diverse languages (Ro,Lv, Fi, Tr and Ko) as target tasks. We show that the proposed approach significantly outperforms the multilingual, transfer learning based approach (Zoph et al., 2016) and enables us to train a competitive NMT system with only a fraction of training examples. For instance, the proposed approach can achieve as high as 22.04 BLEU on Romanian-English WMT’16 by seeing only 16,000 translated words (~600 parallel sentences)


pdf bib
Introduction to NJUPT Chinese Spelling Check Systems in CLP-2014 Bakeoff
Lei Gu | Yong Wang | Xitao Liang
Proceedings of The Third CIPS-SIGHAN Joint Conference on Chinese Language Processing


pdf bib
Identify Temporal Websites Based on User Behavior Analysis
Yong Wang | Yiqun Liu | Min Zhang | Shaoping Ma | Liyun Ru
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I