Yijun Wang


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Few Clean Instances Help Denoising Distant Supervision
Yufang Liu | Ziyin Huang | Yijun Wang | Changzhi Sun | Man Lan | Yuanbin Wu | Xiaofeng Mou | Ding Wang
Proceedings of the 29th International Conference on Computational Linguistics

Existing distantly supervised relation extractors usually rely on noisy data for both model training and evaluation, which may lead to garbage-in-garbage-out systems. To alleviate the problem, we study whether a small clean dataset could help improve the quality of distantly supervised models. We show that besides getting a more convincing evaluation of models, a small clean dataset also helps us to build more robust denoising models. Specifically, we propose a new criterion for clean instance selection based on influence functions. It collects sample-level evidence for recognizing good instances (which is more informative than loss-level evidence). We also propose a teacher-student mechanism for controlling purity of intermediate results when bootstrapping the clean set. The whole approach is model-agnostic and demonstrates strong performances on both denoising real (NYT) and synthetic noisy datasets.


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UniRE: A Unified Label Space for Entity Relation Extraction
Yijun Wang | Changzhi Sun | Yuanbin Wu | Hao Zhou | Lei Li | Junchi Yan
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Many joint entity relation extraction models setup two separated label spaces for the two sub-tasks (i.e., entity detection and relation classification). We argue that this setting may hinder the information interaction between entities and relations. In this work, we propose to eliminate the different treatment on the two sub-tasks’ label spaces. The input of our model is a table containing all word pairs from a sentence. Entities and relations are represented by squares and rectangles in the table. We apply a unified classifier to predict each cell’s label, which unifies the learning of two sub-tasks. For testing, an effective (yet fast) approximate decoder is proposed for finding squares and rectangles from tables. Experiments on three benchmarks (ACE04, ACE05, SciERC) show that, using only half the number of parameters, our model achieves competitive accuracy with the best extractor, and is faster.

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ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction
Yijun Wang | Changzhi Sun | Yuanbin Wu | Hao Zhou | Lei Li | Junchi Yan
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Current state-of-the-art systems for joint entity relation extraction (Luan et al., 2019; Wad-den et al., 2019) usually adopt the multi-task learning framework. However, annotations for these additional tasks such as coreference resolution and event extraction are always equally hard (or even harder) to obtain. In this work, we propose a pre-training method ENPAR to improve the joint extraction performance. ENPAR requires only the additional entity annotations that are much easier to collect. Unlike most existing works that only consider incorporating entity information into the sentence encoder, we further utilize the entity pair information. Specifically, we devise four novel objectives,i.e., masked entity typing, masked entity prediction, adversarial context discrimination, and permutation prediction, to pre-train an entity encoder and an entity pair encoder. Comprehensive experiments show that the proposed pre-training method achieves significant improvement over BERT on ACE05, SciERC, and NYT, and outperforms current state-of-the-art on ACE05.


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汉语学习者依存句法树库构建(Construction of a Treebank of Learner Chinese)
Jialu Shi (师佳璐) | Xinyu Luo (罗昕宇) | Liner Yang (杨麟儿) | Dan Xiao (肖丹) | Zhengsheng Hu (胡正声) | Yijun Wang (王一君) | Jiaxin Yuan (袁佳欣) | Yu Jingsi (余婧思) | Erhong Yang (杨尔弘)
Proceedings of the 19th Chinese National Conference on Computational Linguistics


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Pre-training Entity Relation Encoder with Intra-span and Inter-span Information
Yijun Wang | Changzhi Sun | Yuanbin Wu | Junchi Yan | Peng Gao | Guotong Xie
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we integrate span-related information into pre-trained encoder for entity relation extraction task. Instead of using general-purpose sentence encoder (e.g., existing universal pre-trained models), we introduce a span encoder and a span pair encoder to the pre-training network, which makes it easier to import intra-span and inter-span information into the pre-trained model. To learn the encoders, we devise three customized pre-training objectives from different perspectives, which target on tokens, spans, and span pairs. In particular, a span encoder is trained to recover a random shuffling of tokens in a span, and a span pair encoder is trained to predict positive pairs that are from the same sentences and negative pairs that are from different sentences using contrastive loss. Experimental results show that the proposed pre-training method outperforms distantly supervised pre-training, and achieves promising performance on two entity relation extraction benchmark datasets (ACE05, SciERC).


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AntNLP at CoNLL 2018 Shared Task: A Graph-Based Parser for Universal Dependency Parsing
Tao Ji | Yufang Liu | Yijun Wang | Yuanbin Wu | Man Lan
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

We describe the graph-based dependency parser in our system (AntNLP) submitted to the CoNLL 2018 UD Shared Task. We use bidirectional lstm to get the word representation, then a bi-affine pointer networks to compute scores of candidate dependency edges and the MST algorithm to get the final dependency tree. From the official testing results, our system gets 70.90 LAS F1 score (rank 9/26), 55.92 MLAS (10/26) and 60.91 BLEX (8/26).