Tong Zhu


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MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking
Tianwen Tang | Tong Zhu | Haodong Liu | Yin Bai | Jia Cheng | Wenliang Chen
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Zero-shot dialogue state tracking (DST) transfers knowledge to unseen domains, reducing the cost of annotating new datasets. Previous zero-shot DST models mainly suffer from domain transferring and partial prediction problems. To address these challenges, we propose Mixture of Prefix Experts (MoPE) to establish connections between similar slots in different domains, which strengthens the model transfer performance in unseen domains. Empirical results demonstrate that MoPE-DST achieves the joint goal accuracy of 57.13% on MultiWOZ2.1 and 55.4.


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基于不完全标注的自监督多标签文本分类(Self-Training With Incomplete Labeling For Multi-Label Text Classification)
Junfei Ren (任俊飞) | Tong Zhu (朱桐) | Wenliang Chen (陈文亮)
Proceedings of the 22nd Chinese National Conference on Computational Linguistics

“多标签文本分类((Multi-Label Text Classification, MLTC)旨在从预定义的候选标签集合中选择一个或多个文本对应的类别,是自然语言处理C)旨在从预定义的候选标签集合中选择一个或多个文本对应的类别,是自然语言处理(Natural Language Processing,NLP)的一项基本任务。前人工作大多基于规范且全面的标注数据集,而这些规范数据集需要严格的质量控制,一般很难获取。在真实的标注过程中,难免会丢失掉一些相关标签,进而导致不完全标注问题。为此本文提出了一种基于局部标注的自监督框架(Partial Self-Training,PST),该框架利用教师模型自动地给大规模无标注数据打伪标签,同时给不完全标注数据补充缺失标签,最后再利用这些数据反向更新教师模型。在合成数据集和真实数据集上的实验表明,本文提出的PST框架兼容现有的各类多标签文本分类模型,并且可以缓解不完全标注数据对模型的影响。”

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基于句法特征的事件要素抽取方法(Syntax-aware Event Argument Extraction )
Zijian Yu (余子健) | Tong Zhu (朱桐) | Wenliang Chen (陈文亮)
Proceedings of the 22nd Chinese National Conference on Computational Linguistics

“事件要素抽取(Event Argument Extraction, EAE)旨在从非结构化文本中提取事件参与要素。编码器—解码器(Encoder-Decoder)框架是处理该任务的一种常见策略,此前的研究大多只向编码器端输入文本的字词信息,导致模型泛化和远程依赖处理能力较弱。为此,本文提出一种融入句法信息的事件要素抽取模型。首先对文本分析得到成分句法解析树,将词性标签和各节点的句法成分标签编码,增强模型的文本表征能力。然后,本文提出了一种基于树结构的注意力机制(Tree-Attention)辅助模型更好地感知结构化语义信息,提高模型处理远距离依赖的能力。实验结果表明,本文所提方法相较于基线系统F1值提升2.02%,证明该方法的有效性。”

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Mirror: A Universal Framework for Various Information Extraction Tasks
Tong Zhu | Junfei Ren | Zijian Yu | Mengsong Wu | Guoliang Zhang | Xiaoye Qu | Wenliang Chen | Zhefeng Wang | Baoxing Huai | Min Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Sharing knowledge between information extraction tasks has always been a challenge due to the diverse data formats and task variations. Meanwhile, this divergence leads to information waste and increases difficulties in building complex applications in real scenarios. Recent studies often formulate IE tasks as a triplet extraction problem. However, such a paradigm does not support multi-span and n-ary extraction, leading to weak versatility. To this end, we reorganize IE problems into unified multi-slot tuples and propose a universal framework for various IE tasks, namely Mirror. Specifically, we recast existing IE tasks as a multi-span cyclic graph extraction problem and devise a non-autoregressive graph decoding algorithm to extract all spans in a single step. It is worth noting that this graph structure is incredibly versatile, and it supports not only complex IE tasks, but also machine reading comprehension and classification tasks. We manually construct a corpus containing 57 datasets for model pretraining, and conduct experiments on 30 datasets across 8 downstream tasks. The experimental results demonstrate that our model has decent compatibility and outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings. The code, model weights, and pretraining corpus are available at .

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Estimating the Likelihood of Words Being Known with Corpus Analysis and K-Means Clustering Algorithm
Tong Zhu | Derek Irwin | Yanhui Zhang | Renjie Wu | Xiaoyi Jiang
Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation


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Improving Relation Extraction with Relational Paraphrase Sentences
Junjie Yu | Tong Zhu | Wenliang Chen | Wei Zhang | Min Zhang
Proceedings of the 28th International Conference on Computational Linguistics

Supervised models for Relation Extraction (RE) typically require human-annotated training data. Due to the limited size, the human-annotated data is usually incapable of covering diverse relation expressions, which could limit the performance of RE. To increase the coverage of relation expressions, we may enlarge the labeled data by hiring annotators or applying Distant Supervision (DS). However, the human-annotated data is costly and non-scalable while the distantly supervised data contains many noises. In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences. Based on an existing labeled data, we first automatically build a task-specific paraphrase data. Then, we propose a novel model to learn the information of diverse relation expressions. In our model, we try to capture this information on the paraphrases via a joint learning framework. Finally, we conduct experiments on a widely used dataset and the experimental results show that our approach is effective to improve the performance on relation extraction, even compared with a strong baseline.

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Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
Tong Zhu | Haitao Wang | Junjie Yu | Xiabing Zhou | Wenliang Chen | Wei Zhang | Min Zhang
Proceedings of the 28th International Conference on Computational Linguistics

In recent years, distantly-supervised relation extraction has achieved a certain success by using deep neural networks. Distant Supervision (DS) can automatically generate large-scale annotated data by aligning entity pairs from Knowledge Bases (KB) to sentences. However, these DS-generated datasets inevitably have wrong labels that result in incorrect evaluation scores during testing, which may mislead the researchers. To solve this problem, we build a new dataset NYTH, where we use the DS-generated data as training data and hire annotators to label test data. Compared with the previous datasets, NYT-H has a much larger test set and then we can perform more accurate and consistent evaluation. Finally, we present the experimental results of several widely used systems on NYT-H. The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different. This indicates that our human-annotated data is necessary for evaluation of distantly-supervised relation extraction.