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Xiaoyu Xing

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2 papers

1 author row

IJCAI Conference 2019 Conference Paper

Learning Task-Specific Representation for Novel Words in Sequence Labeling

Minlong Peng
Qi Zhang
Xiaoyu Xing
Tao Gui
Jinlan Fu
Xuanjing Huang

Word representation is a key component in neural-network-based sequence labeling systems. However, representations of unseen or rare words trained on the end task are usually poor for appreciable performance. This is commonly referred to as the out-of-vocabulary (OOV) problem. In this work, we address the OOV problem in sequence labeling using only training data of the task. To this end, we propose a novel method to predict representations for OOV words from their surface-forms (e. g. , character sequence) and contexts. The method is specifically designed to avoid the error propagation problem suffered by existing approaches in the same paradigm. To evaluate its effectiveness, we performed extensive empirical studies on four part-of-speech tagging (POS) tasks and four named entity recognition (NER) tasks. Experimental results show that the proposed method can achieve better or competitive performance on the OOV problem compared with existing state-of-the-art methods.

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AAAI Conference 2019 Conference Paper

Trainable Undersampling for Class-Imbalance Learning

Minlong Peng
Qi Zhang
Xiaoyu Xing
Tao Gui
Xuanjing Huang
Yu-Gang Jiang
Keyu Ding
Zhigang Chen

Undersampling has been widely used in the class-imbalance learning area. The main deficiency of most existing undersampling methods is that their data sampling strategies are heuristic-based and independent of the used classifier and evaluation metric. Thus, they may discard informative instances for the classifier during the data sampling. In this work, we propose a meta-learning method built on the undersampling to address this issue. The key idea of this method is to parametrize the data sampler and train it to optimize the classification performance over the evaluation metric. We solve the non-differentiable optimization problem for training the data sampler via reinforcement learning. By incorporating evaluation metric optimization into the data sampling process, the proposed method can learn which instance should be discarded for the given classifier and evaluation metric. In addition, as a data level operation, this method can be easily applied to arbitrary evaluation metric and classifier, including non-parametric ones (e. g. , C4. 5 and KNN). Experimental results on both synthetic and realistic datasets demonstrate the effectiveness of the proposed method.

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