Papers by Zhangming Li
Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders (D19-1)
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| Challenge: | Existing work on variableal autoencoders and waterstein autoencoding models has shown significant progress in open-domain response generation. |
| Approach: | They propose to embed user-level and utterance-level information into two multimodal distributions and combine them into a mixed distribution. |
| Outcome: | The proposed model outperforms state-of-the-art models on a large-scale real-world dataset. |
LoRaDA: Low-Rank Direct Attention Adaptation for Efficient LLM Fine-tuning (2025.findings-emnlp)
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| Challenge: | Recent advances in parameter-efficient fine-tuning techniques allow for adjustments to only a minor fraction of the parameters of language models. |
| Approach: | They propose a low-rank direct attention adapted method for efficient LLM fine-tuning . they propose LMAM, which can bring negative attention to self-attention modules . |
| Outcome: | The proposed method outperforms the full fine-tuning method by 2.1% on GLUE benchmark. |
VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles (2020.emnlp-main)
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| Challenge: | Existing studies show that multimodal news can significantly improve users' sense of satisfaction for informativeness. |
| Approach: | They propose a task of Video-based Multimodal Summarization with Multimodal Output to solve this problem. |
| Outcome: | The proposed method can generate multimodal summaries with a single input . it can model the temporal dependency of video with semantic meaning of article . |
Enhancing the Open-Domain Dialogue Evaluation in Latent Space (2021.findings-acl)
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| Challenge: | Existing methods to evaluate opendomain dialogues are limited due to the one-to-many nature of dialogues. |
| Approach: | They propose a self-supervised setting to obtain a smooth latent space that captures discourse-level context information and implicitly models more references in latent spaces. |
| Outcome: | The proposed method outperforms baseline methods on two real-world dialogue datasets. |
How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing (D19-1)
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| Challenge: | Extensive experiments on a large-scale real-world text summarization dataset show that PESG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations. |
| Approach: | They propose a model that learns summary patterns and prototype facts from a prototype document . they use a fact checker to estimate mutual information between the input document and generated summary . |
| Outcome: | Experiments on a large-scale real-world text summarization dataset show that PESG achieves state-of-the-art performance. |
Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation (D19-1)
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Zhangming Chan, Xiuying Chen, Yongliang Wang, Juntao Li, Zhiqiang Zhang, Kun Gai, Dongyan Zhao, Rui Yan
| Challenge: | Existing models for product description generation do not take the product attribute information into account. |
| Approach: | They propose a model that takes the embedding and the entity label of each word into account . they establish a keyword memory that stores the entity labels as keys and keywords as values . |
| Outcome: | The proposed model increases the fidelity of the generated descriptions by 25%. |