Papers by Shiliang Pu
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. |
LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin (2024.acl-long)
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Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Wei Shen, Limao Xiong, Yuhao Zhou, Xiao Wang, Zhiheng Xi, Xiaoran Fan, Shiliang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM. |
| Approach: | They propose a framework that introduces several low-rank adapters and integrates them by using a router network to freeze the backbone model and force a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks. |
| Outcome: | The proposed framework freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting. |
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding (2022.acl-long)
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Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, Shiliang Pu, Fei Wu
| Challenge: | Existing methods for grounding video frames with dense annotations require enormous amount of human effort. |
| Approach: | They propose to ground natural language in video frames with only one frame labeled . they propose an end-to-end model that eliminates interference of irrelevant frames . |
| Outcome: | The proposed model can ground natural language in all video frames with only one frame labeled . the proposed model eliminates interference of irrelevant frames based on branch search and cropping techniques . |
Insert or Attach: Taxonomy Completion via Box Embedding (2024.acl-long)
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| Challenge: | Existing taxonomy expansion methods embed concepts as vectors in Euclidean space, causing incorrectly model asymmetric relations. |
| Approach: | They propose to use box containment and center closeness to create geometric scorers that capture intrinsic relationships between concepts. |
| Outcome: | The proposed framework outperforms existing methods on four real-world datasets. |
MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition (2023.emnlp-main)
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| Challenge: | Distantly supervised named entity recognition (DS-NER) aims to locate entity mentions and classify their types with knowledge bases or gazetteers and unlabeled corpus. |
| Approach: | They propose a noise-robust prototype network named MProto for a DS-NER task . they propose an optimal transport algorithm to mitigate the noise from incomplete labeling . |
| Outcome: | The proposed network achieves state-of-the-art on several DS-NER benchmarks. |
Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER (2022.coling-1)
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| Challenge: | Existing methods to extract entities from visually-rich documents ignore the inherent multimodality of VRDs and thus the suboptimal results are achieved. |
| Approach: | They propose a multimodal semantic enhancement method that filters redundant information in the current document and a cross-document information awareness technique to enrich the entity-related context. |
| Outcome: | The proposed method outperforms existing methods on two documents understanding benchmarks covering eight languages. |
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. |
Multi-modal Action Chain Abductive Reasoning (2023.acl-long)
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Mengze Li, Tianbao Wang, Jiahe Xu, Kairong Han, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Shiliang Pu, Fei Wu
| Challenge: | Existing models for Abductive Reasoning are limited in their ability to infer the most plausible explanation of incomplete known phenomena. |
| Approach: | They propose a vision-language task that aims to imagine the most plausible event by spatio-temporal grounding in past video and infer the hypothesis of subsequent action chain layer by layer. |
| Outcome: | The proposed model outperforms existing video-language models in terms of effectiveness on the proposed dataset. |
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. |
Posterior-regularized REINFORCE for Instance Selection in Distant Supervision (N19-1)
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| Challenge: | Existing methods to train unbiased methods such as REINFORCE take time to train. |
| Approach: | They propose to use posterior regularization to integrate domain-specific rules in instance selection using REINFORCE to improve the performance of the relation classifier trained on cleaned distant supervision datasets. |
| Outcome: | The proposed method improves the performance of the relation classifier trained on cleaned distant supervision dataset as well as the efficiency of the REINFORCE training. |
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. |