Papers by Rong Zhou
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection (2021.emnlp-main)
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| Challenge: | Existing studies on aspect-level sentiment analysis focus on extracting aspect terms and sentiment polarities separately. |
| Approach: | They propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-dimensional aspect-level sentiment analysis. |
| Outcome: | The proposed approach can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects. |
MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)
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Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou
| Challenge: | Existing datasets exhibit data scarcity and limited coverage of general-domain events. |
| Approach: | They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types. |
| Outcome: | The proposed dataset shows that existing methods cannot achieve promising results on the small datasets. |
Enhancing Neural Models with Vulnerability via Adversarial Attack (2020.coling-main)
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| Challenge: | Existing work on adversarial attack to improve performance of NLSM tasks has not been done. |
| Approach: | They propose a general two-stage training framework to enhance neural models with Vulnerability via adversarial attack. |
| Outcome: | The proposed framework improves neural models with Vulnerability via adversarial attack on NLSM datasets. |
Reinforcement Learning with Semantic Rewards Enables Low-Resource Language Expansion without Alignment Tax (2026.findings-acl)
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Zeli Su, Ziyin Zhang, Zhou Liu, Xuexian Song, Zhankai Xu, Longfei Zheng, Xiaolu Zhang, Rong Fu, Guixian Xu, Wentao Zhang
| Challenge: | Extending large language models to low-resource languages often incurs an "alignment tax" token-level fine-tuning enforces token-level surface imitation on narrow and biased data distributions. |
| Approach: | They propose a semantic-space alignment paradigm powered by group-level semantic rewards instead of likelihood maximization. |
| Outcome: | The proposed model acquires low-resource capa- bilities while mitigating alignment tax on Tibetan–Chinese machine translation and Ti- betan headline generation. |
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. |
Robust Lottery Tickets for Pre-trained Language Models (2022.acl-long)
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| Challenge: | Recent studies have shown that pre-trained language models contain smaller matching subnetworks that are not robust to adversarial examples. |
| Approach: | They propose a method to find robust tickets hidden in pre-trained language models by learning binary weight masks and an adversarial loss objective to guide the search. |
| Outcome: | The proposed method improves on previous work on adversarial robustness evaluation. |
Focusing, Bridging and Prompting for Few-shot Nested Named Entity Recognition (2023.findings-acl)
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| Challenge: | Existing work on few-shot named entity recognition (NER) addresses flat entities instead of nested entities. |
| Approach: | They propose a method based on focusing, bridging and prompting for few-shot nested NER without using source domain data. |
| Outcome: | The proposed method outperforms baseline models on four benchmark datasets and outperformed several competing models on F1-score by 9.33% on ACE2004, 6.17% on ace2005, 9.40% on GENIA and 5.12% on KBP2017. |
DUB: Discrete Unit Back-translation for Speech Translation (2023.findings-acl)
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| Challenge: | Discrete unit back-translation (DUB) is a back-translated speech-to-text translation (ST) technique that can be applied to ST . a modality gap between speech and text makes it difficult to transfer these techniques to ST due to the modality of the speech-text model. |
| Approach: | They propose a method to represent speech with discrete units instead of continuous features in direct ST. |
| Outcome: | The proposed method achieves comparable performance to existing methods that rely on large-scale external data. |
Mixture of Attention Heads: Selecting Attention Heads Per Token (2022.emnlp-main)
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| Challenge: | Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing. |
| Approach: | They propose a new architecture that combines multi-head attention with the MoE mechanism and a sparsely gated architecture that allows for faster computations. |
| Outcome: | The proposed architecture can scale up the number of attention heads and the number parameters while preserving computational efficiency. |
A Survey of Large Language Model-Based Search Agents (2026.acl-long)
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Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have revolutionized web search, but their integration is static and cannot handle complex contexts. |
| Approach: | They analyze existing research and analyze existing work from the perspectives of architecture, optimization, application, and evaluation. |
| Outcome: | The proposed models can comprehend user intentions and context and execute multi-turn retrieval with dynamic planning, extending search capabilities far beyond the web. |