Shirong Shen


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Event Causality Identification via Derivative Prompt Joint Learning
Shirong Shen | Heng Zhou | Tongtong Wu | Guilin Qi
Proceedings of the 29th International Conference on Computational Linguistics

This paper studies event causality identification, which aims at predicting the causality relation for a pair of events in a sentence. Regarding event causality identification as a supervised classification task, most existing methods suffer from the problem of insufficient annotated data. In this paper, we propose a new derivative prompt joint learning model for event causality identification, which leverages potential causal knowledge in the pre-trained language model to tackle the data scarcity problem. Specifically, rather than external data or knowledge augmentation, we derive two relevant prompt tasks from event causality identification to enhance the model’s ability to identify explicit and implicit causality. We evaluate our model on two benchmark datasets and the results show that our model has great advantages over previous methods.


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Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection
Shirong Shen | Tongtong Wu | Guilin Qi | Yuan-Fang Li | Gholamreza Haffari | Sheng Bi
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Simple or Complex? Complexity-controllable Question Generation with Soft Templates and Deep Mixture of Experts Model
Sheng Bi | Xiya Cheng | Yuan-Fang Li | Lizhen Qu | Shirong Shen | Guilin Qi | Lu Pan | Yinlin Jiang
Findings of the Association for Computational Linguistics: EMNLP 2021

The ability to generate natural-language questions with controlled complexity levels is highly desirable as it further expands the applicability of question generation. In this paper, we propose an end-to-end neural complexity-controllable question generation model, which incorporates a mixture of experts (MoE) as the selector of soft templates to improve the accuracy of complexity control and the quality of generated questions. The soft templates capture question similarity while avoiding the expensive construction of actual templates. Our method introduces a novel, cross-domain complexity estimator to assess the complexity of a question, taking into account the passage, the question, the answer and their interactions. The experimental results on two benchmark QA datasets demonstrate that our QG model is superior to state-of-the-art methods in both automatic and manual evaluation. Moreover, our complexity estimator is significantly more accurate than the baselines in both in-domain and out-domain settings.


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Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism
Shirong Shen | Guilin Qi | Zhen Li | Sheng Bi | Lusheng Wang
Proceedings of the 28th International Conference on Computational Linguistics

Event extraction plays an important role in legal applications, including case push and auxiliary judgment. However, traditional event structure cannot express the connections between arguments, which are extremely important in legal events. Therefore, this paper defines a dynamic event structure for Chinese legal events. To distinguish between similar events, we design hierarchical event features for event detection. Moreover, to address the problem of long-distance semantic dependence and anaphora resolution in argument classification, we propose a novel pedal attention mechanism to extract the semantic relation between two words through their dependent adjacent words. We label a Chinese legal event dataset and evaluate our model on it. Experimental results demonstrate that our model can surpass other state-of-the-art models.


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Customized Attention Mechanism for Relation Classification
Shirong Shen | Yang Wen | Lijuan Zhou | Hongyin Zan
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation