Papers by Chenyi Liu
Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders (P18-1)
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| Challenge: | Extensive experiments show that typed decoders outperform state-of-the-art baselines and can generate more meaningful questions. |
| Approach: | They devised two typed decoders that generate questions with different types of interrogatives, topic words, and ordinary words. |
| Outcome: | Extensive experiments show that the typed decoders outperform state-of-the-art baselines and can generate more meaningful questions. |
Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering (2025.acl-short)
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| Challenge: | Current approaches generate visual markers for all questions, generating excessive visual markers. |
| Approach: | They propose a plug-and-play approach that adapts to the complexity of questions . they propose combining fast intuitive judgments with deliberate analytical reasoning . |
| Outcome: | The proposed approach improves performance on four benchmarks on ScienceQA, TextQA, VizWiz, and MME. |
MedThink: A Rationale-Guided Framework for Explaining Medical Visual Question Answering (2025.findings-naacl)
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| Challenge: | Existing models for medical visual question answering are limited in their interpretation and interpretation . a semi-automated annotation process is used to streamline data preparation and build new benchmark datasets . |
| Approach: | They propose a semi-automated annotation process to streamline data preparation and build new benchmark Med-VQA datasets. |
| Outcome: | The proposed method achieves an accuracy of 83.5% on R-RAD, 86.3% on RSLAKE and 87.2% on RPath. |