Papers by Chun Yang
Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding (2024.lrec-main)
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| Challenge: | Existing knowledge graphs lack rich inference patterns and the limited ability to model arbitrary timestamps continuously. |
| Approach: | They propose a temporal knowledge graph-based temporal representation method that decomposes time information by polynomials and then enhances the model's capability to represent arbitrary timestamps flexibly. |
| Outcome: | The proposed method can encode arbitrary time information or even unseen timestamps while capturing rich inference patterns and higher-arity relations of the knowledge base. |
Improving Multi-Criteria Chinese Word Segmentation through Learning Sentence Representation (2023.findings-emnlp)
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| Challenge: | Recent Chinese word segmentation models tend to learn the segmentation knowledge through in-vocabulary words rather than understanding the meaning of the entire context. |
| Approach: | They propose a context-aware approach that incorporates unsupervised sentence representation learning over different dropout masks into the multi-criteria training framework. |
| Outcome: | The proposed approach achieves state-of-the-art (SoTA) performance on six of the nine CWS benchmark datasets and out-of vocabulary (OOV) recalls for eight of nine. |
Learning to Think on Hypergraph: HyperCoT for Structure-Guided N-ary Knowledge Graph Completion (2026.acl-long)
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| Challenge: | Existing methods to solve knowledge hypergraph link prediction problem are limited by their ability to generate chain-of-thought (CoT) representations. |
| Approach: | They propose a structure-aware approach that models multi-hop structural reasoning as a depth-sensitive progressive evidence accumulation process. |
| Outcome: | Experiments on three real-world datasets show that HyperCoT outperforms strong n-ary KGC baselines while yielding interpretable multi-hop reasoning traces. |
LET: Leveraging Error Type Information for Grammatical Error Correction (2023.findings-acl)
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| Challenge: | Existing methods for grammatical error correction (GEC) are mainly divided into detection-based and end-to-end generative models. |
| Approach: | They propose an end-to-end framework which Leverages Error Type (LET) information in the generation process to introduce more convincing error type information. |
| Outcome: | The proposed framework outperforms existing methods on various datasets by a clear margin. |
Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints (2022.naacl-main)
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| Challenge: | Existing approaches to lexically constrained neural machine translation suffer from high latency. |
| Approach: | They propose a plug-in algorithm for non-autoregressive translation for this problem . they propose ACT to familiarize the model with the source-side context of constraints . |
| Outcome: | The proposed model improves over the backbone constrained NAT model in constraint preservation and translation quality, especially for rare constraints. |
Triggerless Backdoor Attack for NLP Tasks with Clean Labels (2022.naacl-main)
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Leilei Gan, Jiwei Li, Tianwei Zhang, Xiaoya Li, Yuxian Meng, Fei Wu, Yi Yang, Shangwei Guo, Chun Fan
| Challenge: | Backdoor attacks are a new threat to neural natural language processing models due to the fragility and lack of interpretability of NLP models. |
| Approach: | They propose a method to perform backdoor attacks without an external trigger . they propose to use clean-labeled examples to generate poisoned clean-labelled examples . |
| Outcome: | The proposed strategy is effective and hard to defend due to its triggerless nature. |
SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) lack structural information and semantic context to infer missing entities . large language models often lack structural signals to infuse missing entities into knowledge graphs . |
| Approach: | a modular framework integrates structural information and semantic context into a frozen LLM backbone for link prediction. |
| Outcome: | a new framework integrates KG-derived structural information and semantic context to infer missing entities. |
Bridge the Gap: High-level Semantic Planning for Image Captioning (2020.coling-main)
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| Challenge: | Recent image captioning models have improved the multi-modal interaction, such as attention mechanisms. |
| Approach: | They propose a high-level semantic planning mechanism that integrates a semantic reconstruction and an explicit order planning mechanism to bridge the gap between visual and language domains. |
| Outcome: | The proposed model outperforms previous methods and achieves the state-of-the-art performance on MS COCO. |