Papers by Kan Zhou
What’s Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews (2026.acl-long)
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| Challenge: | Existing efforts to detect factually incorrect content are omitted by creators who subtly reshape impressions by omitting crucial background context. |
| Approach: | They propose a multi-stage pipeline that simulates preview-based and context-based understanding and a OMGuard pipeline that combines interpretation-aware fine-tuning and rationale-guided misleading content correction. |
| Outcome: | The proposed framework lifts an 8B model’s detection accuracy to the level of a 235B LVLM while delivering stronger end-to-end correction. |
CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations (2022.coling-1)
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| Challenge: | Document-grounded dialogs need smooth transitions between knowledge selected for generating responses. |
| Approach: | They propose a multi-document co-referential graph to capture inter- and intra-document relationships . they propose 'Coref-MDG' method to linearize static Coref-mDG into conversational sequence logic. |
| Outcome: | The proposed method outperforms the state-of-the-art by 9.5%, 7.4% and 8.2% on three public benchmarks. |
Exploring Conditional Variational Mechanism to Pinyin Input Method for Addressing One-to-Many Mappings in Low-Resource Scenarios (2024.acl-short)
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| Challenge: | Experimental results demonstrate the superior performance of our method. |
| Approach: | They propose to leverage conditional variational mechanism to simplify pinyin IME . they employ a strategy that facilitates interaction between pinyan and Chinese character information . |
| Outcome: | The proposed method improves the performance of pinyin input method engine (IME) under low-resource conditions. |
SAMP: A Model Inference Toolkit of Post-Training Quantization for Text Processing via Self-Adaptive Mixed-Precision (2023.emnlp-industry)
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| Challenge: | Existing methods for quantization of models are too complicated and can cause performance damage. |
| Approach: | They propose a self-adaptive mixed-precision (SAMP) toolkit to automatically control quantization rate by a mixed-presence architecture to balance model accuracy and efficiency. |
| Outcome: | The proposed toolkit has a higher speedup than PyTorch and FasterTransformer while ensuring the required accuracy. |
Doolittle: Benchmarks and Corpora for Academic Writing Formalization (2023.emnlp-main)
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Shizhe Diao, Yongyu Lei, Liangming Pan, Tianqing Fang, Wangchunshu Zhou, Sedrick Keh, Min-Yen Kan, Tong Zhang
| Challenge: | Existing methods of language refinement focus on narrow, specific linguistic features within isolated sentences, such as grammatical errors and improper word use. |
| Approach: | They propose a task to improve the overall quality of academic writing at paragraph level by integrating automatic feedback into the training process. |
| Outcome: | The proposed task improves the overall quality of formal academic writing at the paragraph level. |