Papers by Lingyu Zhang
How does Attention Affect the Model? (2021.findings-acl)
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| Challenge: | Existing studies on the effectiveness of attention in NLP do not consider changes in semantic capability of different components. |
| Approach: | They propose a framework that exploits a convex hull representation of sequence semantics in an n-dimensional Semantic Euclidean Space and defines indicators to capture the impact of attention on sequence semantic. |
| Outcome: | The proposed framework exploits a convex hull representation of sequence semantics in an n-dimensional Semantic Euclidean Space and defines indicators to capture the impact of attention on sequence semantic. |
M2-TabFact: Multi-Document Multi-Modal Fact Verification with Visual and Textual Representations of Tabular Data (2025.findings-acl)
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| Challenge: | Existing fact-checking systems that can reason over structured data are inefficient compared to humans. |
| Approach: | They propose a multi-modal table-based fact verification task that requires reasoning over visual and textual representations of structured data. |
| Outcome: | The proposed model can reason over visual and textual representations of structured data. |
Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization (P19-1)
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| Challenge: | Existing models for extractive summarization of meetings are unfocused and lack content coverage. |
| Approach: | They propose a multi-modal hierarchical attention model that prioritizes segmentation and summarization . they propose to use multi-level hierarchies to narrow down the focus into topically-relevant segments . |
| Outcome: | The proposed model outperforms the state-of-the-art with BLEU and ROUGE measures. |
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning (2024.findings-acl)
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Kung-Hsiang Huang, Mingyang Zhou, Hou Pong Chan, Yi Fung, Zhenhailong Wang, Lingyu Zhang, Shih-Fu Chang, Heng Ji
| Challenge: | LVLMs are known for producing text that is factually inconsistent with visual input . factuality of generated captions for structured visuals has not been studied as much . |
| Approach: | They propose a typology of factual errors in captions generated by large vision-language models . they propose CHOCOLATE, a visual entailment model that outperforms current models based on this analysis . |
| Outcome: | The proposed model outperforms current models in evaluating caption factuality. |