Daojian Zeng


2024

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Improving Continual Few-shot Relation Extraction through Relational Knowledge Distillation and Prototype Augmentation
Zhiheng Zhang | Daojian Zeng | Xue Bai
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

In this paper, we focus on the challenging yet practical problem of Continual Few-shot Relation Extraction (CFRE), which involves extracting relations in the continuous and iterative arrival of new data with only a few labeled examples. The main challenges in CFRE are overfitting due to few-shot learning and catastrophic forgetting caused by continual learning. To address these problems, we propose a novel framework called RK2DA, which seamlessly integrates prototype-based data augmentation and relational knowledge distillation. Specifically, RK2DA generates pseudo data by introducing Gaussian noise to the prototype embeddings and utilizes a novel two-phase multi-teacher relational knowledge distillation method to transfer various knowledge from different embedding spaces. Experimental results on the FewRel and TACRED datasets demonstrate that our method outperforms the state-of-the-art baselines.

2020

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Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction
Ranran Haoran Zhang | Qianying Liu | Aysa Xuemo Fan | Heng Ji | Daojian Zeng | Fei Cheng | Daisuke Kawahara | Sadao Kurohashi
Findings of the Association for Computational Linguistics: EMNLP 2020

Joint entity and relation extraction aims to extract relation triplets from plain text directly. Prior work leverages Sequence-to-Sequence (Seq2Seq) models for triplet sequence generation. However, Seq2Seq enforces an unnecessary order on the unordered triplets and involves a large decoding length associated with error accumulation. These methods introduce exposure bias, which may cause the models overfit to the frequent label combination, thus limiting the generalization ability. We propose a novel Sequence-to-Unordered-Multi-Tree (Seq2UMTree) model to minimize the effects of exposure bias by limiting the decoding length to three within a triplet and removing the order among triplets. We evaluate our model on two datasets, DuIE and NYT, and systematically study how exposure bias alters the performance of Seq2Seq models. Experiments show that the state-of-the-art Seq2Seq model overfits to both datasets while Seq2UMTree shows significantly better generalization. Our code is available at https://github.com/WindChimeRan/OpenJERE.

2019

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Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning
Xiangrong Zeng | Shizhu He | Daojian Zeng | Kang Liu | Shengping Liu | Jun Zhao
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The multiple relation extraction task tries to extract all relational facts from a sentence. Existing works didn’t consider the extraction order of relational facts in a sentence. In this paper we argue that the extraction order is important in this task. To take the extraction order into consideration, we apply the reinforcement learning into a sequence-to-sequence model. The proposed model could generate relational facts freely. Widely conducted experiments on two public datasets demonstrate the efficacy of the proposed method.

2018

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Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
Xiangrong Zeng | Daojian Zeng | Shizhu He | Kang Liu | Jun Zhao
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The relational facts in sentences are often complicated. Different relational triplets may have overlaps in a sentence. We divided the sentences into three types according to triplet overlap degree, including Normal, EntityPairOverlap and SingleEntiyOverlap. Existing methods mainly focus on Normal class and fail to extract relational triplets precisely. In this paper, we propose an end-to-end model based on sequence-to-sequence learning with copy mechanism, which can jointly extract relational facts from sentences of any of these classes. We adopt two different strategies in decoding process: employing only one united decoder or applying multiple separated decoders. We test our models in two public datasets and our model outperform the baseline method significantly.

2015

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Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
Daojian Zeng | Kang Liu | Yubo Chen | Jun Zhao
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks
Yubo Chen | Liheng Xu | Kang Liu | Daojian Zeng | Jun Zhao
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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Group Non-negative Matrix Factorization with Natural Categories for Question Retrieval in Community Question Answer Archives
Guangyou Zhou | Yubo Chen | Daojian Zeng | Jun Zhao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Sentiment Classification with Graph Co-Regularization
Guangyou Zhou | Jun Zhao | Daojian Zeng
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Relation Classification via Convolutional Deep Neural Network
Daojian Zeng | Kang Liu | Siwei Lai | Guangyou Zhou | Jun Zhao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers