Papers by Rongsheng Zhang
LANID: LLM-assisted New Intent Discovery (2024.lrec-main)
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| Challenge: | Data annotation is expensive in Task-Oriented Dialogue systems. |
| Approach: | They propose a framework that leverages Large Language Models' zero-shot capability to enhance the performance of a smaller text encoder on the NID task. |
| Outcome: | The proposed framework surpasses all strong baselines in both unsupervised and semi-supervised settings. |
Probing Simile Knowledge from Pre-trained Language Models (2022.acl-long)
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Weijie Chen, Yongzhu Chang, Rongsheng Zhang, Jiashu Pu, Guandan Chen, Le Zhang, Yadong Xi, Yijiang Chen, Chang Su
| Challenge: | Existing approaches to learn generic knowledge from a large corpus are time-consuming and labor-intensive. |
| Approach: | They propose a framework to probe simile knowledge from pre-trained language models to solve SI and SG tasks. |
| Outcome: | The proposed framework solves the SI and SG tasks in a simile triple completion task. |
Youling: an AI-assisted Lyrics Creation System (2020.emnlp-demos)
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Rongsheng Zhang, Xiaoxi Mao, Le Li, Lin Jiang, Lin Chen, Zhiwei Hu, Yadong Xi, Changjie Fan, Minlie Huang
| Challenge: | Recent studies have focused on a single pass of lyrics generation with little human intervention. |
| Approach: | They propose an AI-assisted lyrics creation system that supports one pass full-text generation and interactive generation modes. |
| Outcome: | The proposed system supports full-text generation and interactive generation modes . it also provides a revision module which enables users to revise undesired lyrics repeatedly. |
Conditioned Masked Language and Image Modeling for Image-Text Dense Retrieval (2022.findings-emnlp)
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| Challenge: | Large-scale two-stream pre-trained models like CLIP have achieved tremendous success in image-text retrieval. |
| Approach: | They propose a cross-modal framework for image-text retrieval using two-stream pre-trained models . they embed images and texts into instance representations with two separate encoders . experimental results on MSCOCO and Flickr30k reveal the effectiveness of their framework . |
| Outcome: | The proposed framework improves image-text retrieval performance on two popular cross-modal retrieval benchmarks. |
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks (2022.findings-naacl)
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| Challenge: | Experimental results show that Transformer Encoder model can't automatically capture word order, so explicit position embeddings are required to be fed into the target model. |
| Approach: | They propose a Transformer-based language model DecBERT that uses a causal attention mask to capture word order. |
| Outcome: | The proposed model improves on the GLUE language understanding benchmark and accelerates the pre-training process. |
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data (2020.emnlp-main)
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| Challenge: | Existing research has focused on training open-domain dialogue models using unpaired data. |
| Approach: | They propose a data-level distillation method for training open-domain dialogue models by utilizing unpaired data. |
| Outcome: | The proposed method produces high-quality dialogue pairs with diverse contents, and can improve competitive baselines. |
QiuNiu: A Chinese Lyrics Generation System with Passage-Level Input (2022.acl-demo)
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| Challenge: | Existing systems based on attributes or keywords render lyrics generation very limited . previous studies focused on generating lyrics based only on attributes and keywords . |
| Approach: | They propose to use Chinese passage-level text as input for lyrics generation . they initialize parameters with custom pretrained Chinese GPT-2 model and adopt a two-step process to fine-tune the model for better alignment between passage- level text and lyrics. |
| Outcome: | The proposed system is conditioned on passage-level text rather than attributes or keywords, rendering limited control over the content of the lyrics. |
Just Adjust One Prompt: Enhancing In-Context Dialogue Scoring via Constructing the Optimal Subgraph of Demonstrations and Prompts (2023.emnlp-main)
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| Challenge: | Using large language models as chatbots can cause hallucinations and lack of empathy, authors report . a dimension-agnostic scoring method is proposed to improve the performance of chatbot performance . |
| Approach: | They propose a dimension-agnostic scoring method that leverages in-context learning . they propose to automatically generate prompts and then request the LLM multiple times . |
| Outcome: | The proposed method outperforms baselines on five datasets. |
LaMemo: Language Modeling with Look-Ahead Memory (2022.naacl-main)
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| Challenge: | Existing approaches to model long-term dependencies are limited to long texts with thousands of words. |
| Approach: | They propose a look-ahead memory that augments the recurrence memory by attending to the right-side tokens and interpolating with the old memory states to maintain long-term information in the history. |
| Outcome: | Experiments on widely used language modeling benchmarks show that LaMemo outperforms baseline models with recurrence memory. |
PromptNER: Prompt Locating and Typing for Named Entity Recognition (2023.acl-long)
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Yongliang Shen, Zeqi Tan, Shuhui Wu, Wenqi Zhang, Rongsheng Zhang, Yadong Xi, Weiming Lu, Yueting Zhuang
| Challenge: | Existing methods for prompt learning require a multi-round prompting manner and require elaborate templates. |
| Approach: | They propose to unify entity locating and entity typing into prompt learning by enumerating spans to predict their entity types or constructing type-specific prompts to locate entities. |
| Outcome: | The proposed model outperforms the state-of-the-art model in a few-shot setting . it uses a template filled with multiple prompts and a bipartite graph matching mechanism . |
A General Knowledge Injection Framework for ICD Coding (2025.findings-acl)
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| Challenge: | Existing methods to improve ICD coding focus on a single type of knowledge and design specialized modules that are complex and incompatible with each other. |
| Approach: | They propose a general knowledge injection framework that integrates three key types of knowledge without specialized design of additional modules. |
| Outcome: | The proposed framework outperforms baseline models and is comparable to models relying on extra human annotations. |
Sudowoodo: A Chinese Lyric Imitation System with Source Lyrics (2023.emnlp-demo)
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| Challenge: | Existing studies on lyrics generation focus on generating accurate lyrics using keywords, rhymes, etc. However, there is no parallel corpus for lyrics imitation. |
| Approach: | They propose a Chinese lyrics imitation system that can generate new lyrics based on source lyrics. |
| Outcome: | The proposed system can generate new lyrics based on the source lyrics . human evaluation shows it can perform better lyric imitation. |
LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency (2022.coling-1)
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| Challenge: | Existing parameter-efficient methods focus on reducing trainable parameters but neglect the inference speed, which limits the ability to deploy PLMs. |
| Approach: | They propose to use a hypernetwork-assisted inter-layer connector to enhance inference efficiency by tuning parameters inside a linear connector between two Transformer layers. |
| Outcome: | The proposed model reduces model parameters to 11.75% while preserving performance degradation to less than 5%. |
Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues (2025.emnlp-main)
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Jinfeng Zhou, Yuxuan Chen, Jianing Yin, Yongkang Huang, Yihan Shi, Xikun Zhang, Libiao Peng, Rongsheng Zhang, Tangjie Lv, Zhipeng Hu, Hongning Wang, Minlie Huang
| Challenge: | Existing approaches to cognitive restructuring (CR) are limited by entrenched cognitive distortions, emotional resistance, and individual differences. |
| Approach: | They propose a framework that structures CR as theory-grounded multi-stage multi-turn dialogue and a multi-channel loop mechanism to account for diverse individual distortions. |
| Outcome: | The proposed framework integrates supportive strategies for emotional management and a multi-channel loop mechanism to account for diverse individual distortions. |
Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model (2022.naacl-industry)
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Gongzheng Li, Yadong Xi, Jingzhen Ding, Duan Wang, Ziyang Luo, Rongsheng Zhang, Bai Liu, Changjie Fan, Xiaoxi Mao, Zeng Zhao
| Challenge: | Recent studies show that transformer-based models are effective over many tasks, but they are expensive to deploy in the industrial application. |
| Approach: | They propose a transformer-based inference solution that optimizes kernels for long inputs and large hidden sizes and a flexible CUDA memory manager to reduce the memory footprint when deploying a large model. |
| Outcome: | The proposed solution achieves an average speedup of 1.40-4.20x on the transformer decoder layer with an A100 GPU. |
Unraveling the Mystery of Artifacts in Machine Generated Text (2022.lrec-1)
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| Challenge: | Recent studies show that human-written text is not distinguishable from synthetic text because of semantic errors or logical contradictions. |
| Approach: | They propose to analyze the forms of artifacts left by neural Text Generation Models by corrupting texts and replacing them with linguistic or statistical features. |
| Outcome: | The proposed method replaces text with linguistic or statistical features and improves the accuracy of the model. |
HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing (2024.findings-emnlp)
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Jing Chen, Xinyu Zhu, Cheng Yang, Chufan Shi, Yadong Xi, Yuxiang Zhang, Junjie Wang, Jiashu Pu, Tian Feng, Yujiu Yang, Rongsheng Zhang
| Challenge: | Generative AI has demonstrated unprecedented creativity in the field of computer vision, yet such phenomena have not been observed in the realm of literary creation. |
| Approach: | They propose a framework for unleashing the creativity of large language models (LLMs) they assign LLMs to different roles involved in real-world scenario, they write . |
| Outcome: | The proposed framework outperforms baselines in terms of coherence, relevance, interestingness and overall quality on automatically generated screenplays. |
Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging (2025.acl-long)
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Zhenyang Cai, Junying Chen, Rongsheng Wang, Weihong Wang, Yonglin Deng, Dingjie Song, Yize Chen, Zixu Zhang, Benyou Wang
| Challenge: | Current research suggests that multitask training outperforms single-task as different tasks can benefit each other, but they often overlook the internal relationships within these tasks. |
| Approach: | They employ compositional generalization (CG) to examine the generalization of multimodal large language models in medical imaging. |
| Outcome: | The proposed model can understand unseen medical images and is able to perform CG across classification and detection tasks. |