Papers by Hongyu Xiong
PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space (D19-1)
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| Challenge: | Existing approaches to reviewer-paper matching are less effective to deal with the vocabulary mismatch and partial topic overlap between the submission and reviewer. |
| Approach: | They propose to combine the common topic model and abstract topic vectors to model the topics common to the submission and the reviewer's profile while relying on abstract topic vectors. |
| Outcome: | The proposed model improves on the existing model on two datasets. |
Reasoning-Enhanced Domain-Adaptive Pretraining of Multimodal Large Language Models for Short Video Content Governance (2025.emnlp-industry)
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Zixuan Wang, Yu Sun, Hongwei Wang, Baoyu Jing, Xiang Shen, Xin Dong, Zhuolin Hao, Hongyu Xiong, Yang Song
| Challenge: | Existing approaches to identifying inappropriate content require extensive human-labeled data and lack cross-issue generalization. |
| Approach: | They propose a reasoning-enhanced multimodal large language model (MLLM) pretraining paradigm for unified inappropriate content detection. |
| Outcome: | The proposed model improves the MLLM's performance in both zero-shot and supervised fine-tuning settings and shows strong generalization capabilities to emergent, previously unseen issues. |
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus (N19-1)
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| Challenge: | Existing methods for text style transfer have demonstrated considerable success, but a parallel corpus may not always be available for a transfer task. |
| Approach: | They propose a text style transfer model that uses an attention-based encoder-decoder to transfer a sentence from the source style to the target style. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two different style transfer tasks. |
Document Similarity for Texts of Varying Lengths via Hidden Topics (P18-1)
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| Challenge: | Existing approaches to measure document similarity are inadequate for document pairs with non-comparable lengths, such as a long document and its summary. |
| Approach: | They propose a document matching approach to bridge the gap between long documents and their abstract information in a common space of hidden topics. |
| Outcome: | The proposed approach outperforms strong baselines on two matching tasks and incorporates domain knowledge to gain further performance improvement. |
IPS: In-Prompt Process Supervision for Short Video Content Moderation (2026.acl-industry)
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| Challenge: | Multimodal large language models (MLLMs) capture semantics of short video content but fail to account for policy-specific details. |
| Approach: | They propose a framework that integrates In-prompt Process Supervision into MLLMs . they propose sequential reasoning over ancillary questions during fine-tuning . |
| Outcome: | IPS outperforms baseline MLLMs on public and proprietary benchmarks . replacing human-annotated ancillary labels with MLML-generated ones results in performance degradation. |
Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation (2025.acl-industry)
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Zixuan Wang, Jinghao Shi, Hanzhong Liang, Xiang Shen, Vera Wen, Zhiqian Chen, Yifan Wu, Zhixin Zhang, Hongyu Xiong
| Challenge: | Effective content moderation is essential for video platforms to safeguard user experience and uphold community standards. |
| Approach: | They propose a method to transform a generative MLLM into a multimodal classifier using minimal discriminative training data. |
| Outcome: | The proposed method improves F1 score by 66.50% over traditional classifiers while requiring only 2% of the fine-tuning data. |