Papers by Guanglin Niu
Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference (2022.coling-1)
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| Challenge: | Existing knowledge graphs are incomplete and therefore lack interpretability. |
| Approach: | They propose a closed-loop neural-symbolic learning framework EngineKG to address the natural incompleteness of knowledge graphs. |
| Outcome: | The proposed model outperforms baselines on link prediction tasks on four real-world datasets. |
MdEval: Massively Multilingual Code Debugging (2026.findings-acl)
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Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li
| Challenge: | Existing benchmarks primarily focus on Python and are limited in terms of language diversity. |
| Approach: | They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions. |
| Outcome: | The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task. |
AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding (2020.findings-emnlp)
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| Challenge: | Existing knowledge graphs are incomplete whether they are constructed manually or automatically, limiting the effectiveness when exploited for downstream applications. |
| Approach: | They propose a KGE framework with an automatic type embedding mechanism which can be easily integrated into any existing KGE model. |
| Outcome: | The proposed model can model and infer all the relation patterns and complex relations compared to state-of-the-art models on four datasets. |
Entity Concept-enhanced Few-shot Relation Extraction (2021.acl-short)
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| Challenge: | Existing FSRE methods fail to classify relations based on information of sentences and entity pairs due to limited samples and lack of knowledge. |
| Approach: | They propose a concept-sentence attention module to select the most appropriate concept from multiple concepts of each entity by calculating the semantic similarity between sentences and concepts. |
| Outcome: | The proposed scheme outperforms existing methods on a few-shot relation extraction dataset. |
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion (2022.acl-long)
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| Challenge: | Existing knowledge graph embedding techniques rely on fact-view data to predict missing links between entities, limiting their performance. |
| Approach: | They propose a commonsense-aware knowledge embedding framework which generates commonsensense from factual triples with entity concepts for a KGC task. |
| Outcome: | The proposed framework could produce high-quality negative triples and joint commonsense and fact-view link prediction. |