Shu Guo


2023

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Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction
Qian Li | Shu Guo | Cheng Ji | Xutan Peng | Shiyao Cui | Jianxin Li | Lihong Wang
Findings of the Association for Computational Linguistics: ACL 2023

Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities in texts that contain visual clues. Rich visual content is valuable for the MMRE task, but existing works cannot well model finer associations among different modalities, failing to capture the truly helpful visual information and thus limiting relation extraction performance. In this paper, we propose a novel MMRE framework to better capture the deeper correlations of text, entity pair, and image/objects, so as to mine more helpful information for the task, termed as DGF-PT. We first propose a prompt-based autoregressive encoder, which builds the associations of intra-modal and inter-modal features related to the task, respectively by entity-oriented and object-oriented prefixes. To better integrate helpful visual information, we design a dual-gated fusion module to distinguish the importance of image/objects and further enrich text representations. In addition, a generative decoder is introduced with entity type restriction on relations, better filtering out candidates. Extensive experiments conducted on the benchmark dataset show that our approach achieves excellent performance compared to strong competitors, even in the few-shot situation.

2021

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CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction
Jiawei Sheng | Shu Guo | Bowen Yu | Qian Li | Yiming Hei | Lihong Wang | Tingwen Liu | Hongbo Xu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Adaptive Attentional Network for Few-Shot Knowledge Graph Completion
Jiawei Sheng | Shu Guo | Zhenyu Chen | Juwei Yue | Lihong Wang | Tingwen Liu | Hongbo Xu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i.e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries. This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations. Specifically, entities are modeled by an adaptive neighbor encoder to discern their task-oriented roles, while references are modeled by an adaptive query-aware aggregator to differentiate their contributions. Through the attention mechanism, both entities and references can capture their fine-grained semantic meanings, and thus render more expressive representations. This will be more predictive for knowledge acquisition in the few-shot scenario. Evaluation in link prediction on two public datasets shows that our approach achieves new state-of-the-art results with different few-shot sizes. The source code is available at https://github.com/JiaweiSheng/FAAN.

2016

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Jointly Embedding Knowledge Graphs and Logical Rules
Shu Guo | Quan Wang | Lihong Wang | Bin Wang | Li Guo
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Semantically Smooth Knowledge Graph Embedding
Shu Guo | Quan Wang | Bin Wang | Lihong Wang | Li Guo
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)