Jing Tang


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How Fragile is Relation Extraction under Entity Replacements?
Yiwei Wang | Bryan Hooi | Fei Wang | Yujun Cai | Yuxuan Liang | Wenxuan Zhou | Jing Tang | Manjuan Duan | Muhao Chen
Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)

Relation extraction (RE) aims to extract the relations between entity names from the textual context. In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context. However, existing work has found that the RE models memorize the entity name patterns to make RE predictions while ignoring the textual context. This motivates us to raise the question: are RE models robust to the entity replacements? In this work, we operate the random and type-constrained entity replacements over the RE instances in TACRED and evaluate the state-of-the-art RE models under the entity replacements. We observe the 30% - 50% F1 score drops on the state-of-the-art RE models under entity replacements. These results suggest that we need more efforts to develop effective RE models robust to entity replacements. We release the source code at https://github.com/wangywUST/RobustRE.


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NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit
Liqun Liu | Funan Mu | Pengyu Li | Xin Mu | Jing Tang | Xingsheng Ai | Ran Fu | Lifeng Wang | Xing Zhou
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

In this paper, we introduce NeuralClassifier, a toolkit for neural hierarchical multi-label text classification. NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. Built on PyTorch, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature.


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A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss
Wan-Ting Hsu | Chieh-Kai Lin | Ming-Ying Lee | Kerui Min | Jing Tang | Min Sun
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We propose a unified model combining the strength of extractive and abstractive summarization. On the one hand, a simple extractive model can obtain sentence-level attention with high ROUGE scores but less readable. On the other hand, a more complicated abstractive model can obtain word-level dynamic attention to generate a more readable paragraph. In our model, sentence-level attention is used to modulate the word-level attention such that words in less attended sentences are less likely to be generated. Moreover, a novel inconsistency loss function is introduced to penalize the inconsistency between two levels of attentions. By end-to-end training our model with the inconsistency loss and original losses of extractive and abstractive models, we achieve state-of-the-art ROUGE scores while being the most informative and readable summarization on the CNN/Daily Mail dataset in a solid human evaluation.