Papers by Lihong Wang
Prototype-Guided Pseudo Labeling for Semi-Supervised Text Classification (2023.acl-long)
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| Challenge: | Existing semi-supervised text classification methods suffer from categorical boundary issues . existing methods suffer by ambiguous categoric boundaries, making it difficult to generate reliable pseudo-labels for each category. |
| Approach: | They propose a semi-supervised framework that assigns pseudo-labels to unlabeled data . they exploit categorical prototypes to assimilate instance representations within the same category . |
| Outcome: | Empirical studies show that the proposed framework is effective . it uses prototypical cluster separation and prototypical-center data selection . |
Adaptive Attentional Network for Few-Shot Knowledge Graph Completion (2020.emnlp-main)
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| Challenge: | Recent attempts to learn static representations of entities and references ignore their dynamic properties. |
| Approach: | They propose to learn static representations of entities and references ignoring their dynamic properties . a neighbor encoder learns entities' roles while a query-aware aggregator learns references' contributions . |
| Outcome: | The proposed approach achieves state-of-the-art results with different few-shot sizes. |
Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment (2023.findings-emnlp)
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| Challenge: | Multi-modal entity alignment (MMEA) is a critical task that aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs). |
| Approach: | They propose a novel MMEA transformer that hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance alignment task. |
| Outcome: | The proposed transformer hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance the alignment task. |
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture (2023.findings-emnlp)
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Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo Wang
| Challenge: | Existing low-resource learning techniques focus on label annotation while neglecting the natural language explanation of a data point. |
| Approach: | They propose a novel architecture that leverages an explanation-generation model to produce explanations guided by human explanations and a prediction model that utilizes generated explanations toward prediction faithfully. |
| Outcome: | The proposed architecture produces explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a data diversity-based AL sampling strategy that benefits from the explanation annotations. |
Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction (2023.findings-acl)
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| Challenge: | Existing methods for multi-modal relation extraction lack useful visual information. |
| Approach: | They propose a novel multi-modal relation extraction framework to capture deeper correlations of text, entity pair, and image/objects. |
| Outcome: | The proposed framework captures the deeper correlations of text, entity pair, and image/objects, and extracts useful information. |
CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction (2021.findings-acl)
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| Challenge: | Existing methods assume that events appear in sentences without overlaps . overlapping event extraction is a challenging task in natural language understanding . |
| Approach: | They propose a joint learning framework with cascade decoding for overlapping event extraction . they sequentially perform type detection, trigger extraction and argument extraction based on the specific former prediction . |
| Outcome: | The proposed framework improves on a public event extraction benchmark . it sequentially performs type detection, trigger extraction and argument extraction . |
Subgoal Discovery for Hierarchical Dialogue Policy Learning (D18-1)
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| Challenge: | Existing methods to develop dialogue agents for complex tasks require sparse reward signals. |
| Approach: | They propose a divide-and-conquer approach that exploits the hidden structure of a task . they use subgoals to divide a goal-oriented task into simpler subgoal sets . |
| Outcome: | The proposed approach performs competitively against state-of-the-art methods that require human-defined subgoals. |