Papers by Guosheng Liang
Learning distributed sentence vectors with bi-directional 3D convolutions (2020.coling-main)
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| Challenge: | Existing methods that render words or characters into images separately, but instead use text's visual features as input, we use 3-dimensional convolutions to learn distributed sentence representation. |
| Approach: | They propose to use text's visual features as input to learn distributed sentence representation using 3-dimensional sentence tensors and multiple 3-dimensional convolutions with different lengths are applied to the sentence . |
| Outcome: | The proposed model performs well on several downstream natural language processing tasks. |
ThinkSwitcher: When to Think Hard, When to Think Fast (2025.findings-emnlp)
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| Challenge: | Large reasoning models excel at solving complex tasks by leveraging long chain-of-thought (CoT) reasoning. |
| Approach: | They propose a framework that enables a single LRM to dynamically switch between short and long CoT modes based on task complexity. |
| Outcome: | The proposed framework reduces computational cost by 20-30% while maintaining high accuracy on complex tasks. |