Shiliang Sun


2020

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HW-TSC’s Participation in the WMT 2020 News Translation Shared Task
Daimeng Wei | Hengchao Shang | Zhanglin Wu | Zhengzhe Yu | Liangyou Li | Jiaxin Guo | Minghan Wang | Hao Yang | Lizhi Lei | Ying Qin | Shiliang Sun
Proceedings of the Fifth Conference on Machine Translation

This paper presents our work in the WMT 2020 News Translation Shared Task. We participate in 3 language pairs including Zh/En, Km/En, and Ps/En and in both directions under the constrained condition. We use the standard Transformer-Big model as the baseline and obtain the best performance via two variants with larger parameter sizes. We perform detailed pre-processing and filtering on the provided large-scale bilingual and monolingual dataset. Several commonly used strategies are used to train our models such as Back Translation, Ensemble Knowledge Distillation, etc. We also conduct experiment with similar language augmentation, which lead to positive results, although not used in our submission. Our submission obtains remarkable results in the final evaluation.

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HW-TSC’s Participation at WMT 2020 Automatic Post Editing Shared Task
Hao Yang | Minghan Wang | Daimeng Wei | Hengchao Shang | Jiaxin Guo | Zongyao Li | Lizhi Lei | Ying Qin | Shimin Tao | Shiliang Sun | Yimeng Chen
Proceedings of the Fifth Conference on Machine Translation

The paper presents the submission by HW-TSC in the WMT 2020 Automatic Post Editing Shared Task. We participate in the English-German and English-Chinese language pairs. Our system is built based on the Transformer pre-trained on WMT 2019 and WMT 2020 News Translation corpora, and fine-tuned on the APE corpus. Bottleneck Adapter Layers are integrated into the model to prevent over-fitting. We further collect external translations as the augmented MT candidates to improve the performance. The experiment demonstrates that pre-trained NMT models are effective when fine-tuning with the APE corpus of a limited size, and the performance can be further improved with external MT augmentation. Our system achieves competitive results on both directions in the final evaluation.

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HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task
Minghan Wang | Hao Yang | Hengchao Shang | Daimeng Wei | Jiaxin Guo | Lizhi Lei | Ying Qin | Shimin Tao | Shiliang Sun | Yimeng Chen | Liangyou Li
Proceedings of the Fifth Conference on Machine Translation

This paper presents our work in the WMT 2020 Word and Sentence-Level Post-Editing Quality Estimation (QE) Shared Task. Our system follows standard Predictor-Estimator architecture, with a pre-trained Transformer as the Predictor, and specific classifiers and regressors as Estimators. We integrate Bottleneck Adapter Layers in the Predictor to improve the transfer learning efficiency and prevent from over-fitting. At the same time, we jointly train the word- and sentence-level tasks with a unified model with multitask learning. Pseudo-PE assisted QE (PEAQE) is proposed, resulting in significant improvements on the performance. Our submissions achieve competitive result in word/sentence-level sub-tasks for both of En-De/Zh language pairs.

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Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning
Minghan Wang | Hao Yang | Ying Qin | Shiliang Sun | Yao Deng
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

We propose a unified multilingual model for humor detection which can be trained under a transfer learning framework. 1) The model is built based on pre-trained multilingual BERT, thereby is able to make predictions on Chinese, Russian and Spanish corpora. 2) We step out from single sentence classification and propose sequence-pair prediction which considers the inter-sentence relationship. 3) We propose the Sentence Discrepancy Prediction (SDP) loss, aiming to measure the semantic discrepancy of the sequence-pair, which often appears in the setup and punchline of a joke. Our method achieves two SoTA and a second-place on three humor detection corpora in three languages (Russian, Spanish and Chinese), and also improves F1-score by 4%-6%, which demonstrates the effectiveness of it in humor detection tasks.

2019

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Joint Type Inference on Entities and Relations via Graph Convolutional Networks
Changzhi Sun | Yeyun Gong | Yuanbin Wu | Ming Gong | Daxin Jiang | Man Lan | Shiliang Sun | Nan Duan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We develop a new paradigm for the task of joint entity relation extraction. It first identifies entity spans, then performs a joint inference on entity types and relation types. To tackle the joint type inference task, we propose a novel graph convolutional network (GCN) running on an entity-relation bipartite graph. By introducing a binary relation classification task, we are able to utilize the structure of entity-relation bipartite graph in a more efficient and interpretable way. Experiments on ACE05 show that our model outperforms existing joint models in entity performance and is competitive with the state-of-the-art in relation performance.

2018

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Extracting Entities and Relations with Joint Minimum Risk Training
Changzhi Sun | Yuanbin Wu | Man Lan | Shiliang Sun | Wenting Wang | Kuang-Chih Lee | Kewen Wu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We investigate the task of joint entity relation extraction. Unlike prior efforts, we propose a new lightweight joint learning paradigm based on minimum risk training (MRT). Specifically, our algorithm optimizes a global loss function which is flexible and effective to explore interactions between the entity model and the relation model. We implement a strong and simple neural network where the MRT is executed. Experiment results on the benchmark ACE05 and NYT datasets show that our model is able to achieve state-of-the-art joint extraction performances.

2017

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Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network
Changzhi Sun | Yuanbin Wu | Man Lan | Shiliang Sun | Qi Zhang
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We investigate the task of open domain opinion relation extraction. Different from works on manually labeled corpus, we propose an efficient distantly supervised framework based on pattern matching and neural network classifiers. The patterns are designed to automatically generate training data, and the deep learning model is design to capture various lexical and syntactic features. The result algorithm is fast and scalable on large-scale corpus. We test the system on the Amazon online review dataset. The result shows that our model is able to achieve promising performances without any human annotations.