Lihong Wang


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.

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Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment
Qian Li | Cheng Ji | Shu Guo | Zhaoji Liang | Lihong Wang | Jianxin Li
Findings of the Association for Computational Linguistics: EMNLP 2023

Multi-Modal Entity Alignment (MMEA) is a critical task that aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs). However, this task faces challenges due to the presence of different types of information, including neighboring entities, multi-modal attributes, and entity types. Directly incorporating the above information (e.g., concatenation or attention) can lead to an unaligned information space. To address these challenges, we propose a novel MMEA transformer, called Meaformer, that hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance the alignment task. Taking advantage of the transformer’s ability to better integrate multiple information, we design a hierarchical modifiable self-attention block in a transformer encoder to preserve the unique semantics of different information. Furthermore, we design two entity-type prefix injection methods to redintegrate entity-type information using type prefixes, which help to restrict the global information of entities not present in the MMKGs.

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Prototype-Guided Pseudo Labeling for Semi-Supervised Text Classification
Weiyi Yang | Richong Zhang | Junfan Chen | Lihong Wang | Jaein Kim
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Semi-supervised text classification (SSTC) aims at text classification with few labeled data and massive unlabeled data. Recent works achieve this task by pseudo-labeling methods, with the belief that the unlabeled and labeled data have identical data distribution, and assign the unlabeled data with pseudo-labels as additional supervision. However, existing pseudo-labeling methods usually suffer from ambiguous categorical boundary issues when training the pseudo-labeling phase, and simply select pseudo-labels without considering the unbalanced categorical distribution of the unlabeled data, making it difficult to generate reliable pseudo-labels for each category. We propose a novel semi-supervised framework, namely ProtoS2, with prototypical cluster separation (PCS) and prototypical-center data selection (CDS) technology to address the issue. Particularly, PCS exploits categorical prototypes to assimilate instance representations within the same category, thus emphasizing low-density separation for the pseudo-labeled data to alleviate ambiguous boundaries. Besides, CDS selects central pseudo-labeled data considering the categorical distribution, avoiding the model from biasing on dominant categories. Empirical studies and extensive analysis with four benchmarks demonstrate the effectiveness of the proposed model.

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)