Papers by Zhiyuan Wen
GGP: Glossary Guided Post-processing for Word Embedding Learning (2020.lrec-1)
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| Challenge: | Existing word embedding models require much training time and domain knowledge to improve. |
| Approach: | They propose a GGP-based word embedding model that incorporates the glossary and learns sense representations. |
| Outcome: | The proposed model outperforms existing models on topical/functional similarity datasets by 4.1% and 7%. |
Long Text and Multi-Table Summarization: Dataset and Method (2022.findings-emnlp)
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| Challenge: | Existing document summarization methods focus on the text and filter out the non-textual content. Existing methods cannot meet the requirements of summarizing long text and multiple tables in each report. |
| Approach: | They propose a dataset for automatic document summarization that uses text and tabular data to produce a concise summary covering the input document's salient information. |
| Outcome: | The proposed method can produce a concise summary covering the input document's salient information. |
Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking (2020.findings-emnlp)
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| Challenge: | Recent work on sentence prediction tasks uses shallow neural networks to learn essay representations and constrain calculated scores with regression loss or ranking loss. |
| Approach: | They propose to use a pre-trained language model to learn text representations first and then to constrain the scores with regression loss or ranking loss. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Automated Student Assessment Prize dataset. |
Large Language Model-based Human-Agent Collaboration for Complex Task Solving (2024.findings-emnlp)
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| Challenge: | Recent advances in large language models have led to the development of LLM-based autonomous agents. |
| Approach: | They propose a Reinforcement Learning-based Human-Agent Collaboration method which trains a policy model to determine the most opportune stages for human intervention within the task-solving process. |
| Outcome: | The proposed method improves human-agent collaboration significantly through well-planned, limited human intervention. |
Decode with Template: Content Preserving Sentiment Transfer (2020.lrec-1)
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| Challenge: | Existing methods to transfer sentiments for text use only explicit sentiments and templates to remove them from input sentences. |
| Approach: | They propose a method to transfer sentiments from input sentences to output sentences using templates. |
| Outcome: | The proposed model significantly outperforms state-of-the-art models in content preservation. |
Automatically Select Emotion for Response via Personality-affected Emotion Transition (2021.findings-acl)
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| Challenge: | Existing studies focus on rendering specified emotions in responses, yet the individual difference in emotion expression is overlooked. |
| Approach: | They propose to equip a dialog system with personality and enable it to select emotions in responses like humans. |
| Outcome: | The proposed system can select emotions in responses like humans by simulating the emotion transition of humans in conversation. |
Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)
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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao
| Challenge: | Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications. |
| Approach: | They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions. |
| Outcome: | The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions. |
On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)
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Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou
| Challenge: | Pre-trained language models (PLMs) can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but require much more training time than fine-timing. |
| Approach: | They empirically investigate the transferability of soft prompts across different downstream tasks and PLMs to determine what decides prompt transferability. |
| Outcome: | The proposed method can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but requires much more training time than fine-timing. |
Finding Skill Neurons in Pre-trained Transformer-based Language Models (2022.emnlp-main)
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| Challenge: | Pre-trained language models have demonstrated superior performance on various natural language processing tasks. |
| Approach: | They find that after prompt tuning, some neurons encode task-specific skills . they also show that skill neurons are most likely generated in pre-training . |
| Outcome: | The neurons are highly predictive of task labels after prompt tuning for specific tasks. |
ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model (2025.emnlp-main)
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Zhongyi Zhou, Yichen Zhu, Minjie Zhu, Junjie Wen, Ning Liu, Zhiyuan Xu, Weibin Meng, Yaxin Peng, Chaomin Shen, Feifei Feng, Yi Xu
| Challenge: | Recent advances in vision-language-action models prioritize robotic action mastery . however, models trained on visual-text pairs struggle to interpret multimodal data . |
| Approach: | They propose a framework that integrates multimodal data after initial control mastery and a Mixture-of-Experts architecture to minimize task interference. |
| Outcome: | The proposed framework surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks and achieves six times higher performance on visual question-answering datasets. |
Progressive Self-Training with Discriminator for Aspect Term Extraction (2021.emnlp-main)
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| Challenge: | Existing approaches to extract aspect terms from review sentences are limited due to lack of annotated data. |
| Approach: | They propose to refine conventional self-training to progressive self-teaching to reduce noise . they use a discriminator to filter the noisy pseudo-labels. |
| Outcome: | The proposed model outperforms baseline models and achieves state-of-the-art performance on four SemEval datasets. |
Building an Ellipsis-aware Chinese Dependency Treebank for Web Text (L18-1)
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| Challenge: | ellipsis is a common linguistic phenomenon that some words are left out as they are understood from the context, especially in oral utterance. |
| Approach: | They propose to use a Chinese dependency treebank to facilitate the parsing of web text . they propose to restore omissions and reserve contexts in the web text to improve dependency parsers . |
| Outcome: | The proposed framework enables the parsing of web text from online microblogs. |
Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization (2021.findings-acl)
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| Challenge: | Existing models do not consider key phrases in determining attention weights of self-attention . Existing work does not consider the importance of key phrases when determining weights . |
| Approach: | They propose a model with highlighting mechanism to assign greater attention weights to key phrases . they propose two structures of highlighting attention for each head and the multihead highlighting . experimental results show that their proposed model significantly outperforms the baseline model . |
| Outcome: | The proposed model outperforms the baseline models on a multi-news dataset. |