Papers by Yansong Tang
Ponder & Press: Advancing Visual GUI Agent towards General Computer Control (2025.findings-acl)
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| Challenge: | Existing multimodal large language models (MLLMs) lack visual inputs to ground objects, limiting flexibility across diverse software environments and platforms. |
| Approach: | They propose a divide-and-conquer framework for general computer control that uses only visual inputs to create a purely human-like interaction paradigm. |
| Outcome: | The proposed framework outperforms existing models by +22.5% on the ScreenSpot GUI grounding benchmark. |
Understanding Procedural Text using Interactive Entity Networks (2020.emnlp-main)
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| Challenge: | Recent efforts to track multiple entities in a procedural text treat each entity separately . e.g., scientific articles, instruction books, recipes, often contain multiple entities involved . |
| Approach: | They propose a recurrent network with memory equipped cells for state tracking . they maintain different attention matrices through specific memories to model different types of entity interactions . |
| Outcome: | The proposed model outperforms state-of-the-art models on a benchmark dataset. |
Learning to Update Knowledge Graphs by Reading News (D19-1)
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| Challenge: | Existing methods to update knowledge graphs rely on elaborately designed IE systems and domain-specific rules. |
| Approach: | They propose a novel neural network method to update knowledge graphs (KGs) they use a text-based attention mechanism to guide updating messages through KGs . |
| Outcome: | The proposed method can effectively broadcast news information to KG structures and perform necessary link-adding or link-deleting operations to ensure the KG up-to-date according to news snippets. |
Counterfactual Recipe Generation: Exploring Compositional Generalization in a Realistic Scenario (2022.emnlp-main)
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| Challenge: | Existing models fail to learn and use culinary knowledge in a compositional way, argues a new study. |
| Approach: | They propose a task that asks models to modify a base recipe according to the change of an ingredient. |
| Outcome: | The proposed model can perform compositional generalization in a realistic setting . existing models have difficulties in modifying ingredients while preserving original style . |