Papers by Chongyang Shi
Multiview Clickbait Detection via Jointly Modeling Subjective and Objective Preference (2023.findings-emnlp)
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| Challenge: | Existing clickbait detection models rely on analyzing the objective semantics of posts or correlating posts with article content only, but fail to identify and exploit the manipulation intention of clickbaiting from a user’s subjective perspective. |
| Approach: | They propose a multiview clickbait detection model to model subjective and objective preferences simultaneously to capture clickbaiting from a user's subjective perspective. |
| Outcome: | The proposed model outperforms state-of-the-art models on two real-world datasets and shows that it integrates subjective and objective preferences simultaneously. |
Exploring Hyperbolic Hierarchical Structure for Multimodal Rumor Detection (2025.findings-emnlp)
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| Challenge: | rumor detection models often assume a simplistic one-to-one alignment between modalities . authors present a method that preserves hierarchical, non-linear relationships . |
| Approach: | They propose a method that uses hyperbolic geometry to preserve hierarchical relationships . it decomposes image and text content into three levels and embeds them in hyperbolical space . |
| Outcome: | The proposed method preserves hierarchical relationships rather than representing them at a flat semantic level. |
Structural Patent Classification Using Label Hierarchy Optimization (2025.findings-emnlp)
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| Challenge: | Existing methods for patent classification ignore key technical content claims and citation relationships . existing methods treat labels as independent targets, failing to exploit semantic and structural information within the label taxonomy. |
| Approach: | They propose a Claim Structure based Patent Classification model with Label Awareness . structural graph learning is used to mine the internal logic of patent claims . |
| Outcome: | The proposed method is more effective than state-of-the-art classification models. |
Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling (2020.coling-main)
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| Challenge: | Existing joint models for intent detection and slot filling show insufficient robustness . however, some small changes of inputs can fool the models to produce wrong predictions . |
| Approach: | They propose a joint adversarial training model that generates adversarials to attack the joint model and trains the model to defend against the adversarial examples. |
| Outcome: | The proposed model achieves significantly higher scores and improves robustness on two datasets. |
Causal Intervention for Abstractive Related Work Generation (2023.findings-emnlp)
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| Challenge: | Existing models ignore the inherent causality during related work generation, leading to spurious correlations which downgrade the models’ generation quality and generalizability. |
| Approach: | They propose a Causal Intervention Module for Related Work Generation (CaM) that captures causal relationships in related work generation and implements causal interventions to mitigate the negative impact of spurious correlations. |
| Outcome: | The proposed framework improves the quality and coherence of generated related work by capturing causalities in the generation process. |