Papers by Qihang Feng
Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models (2025.emnlp-main)
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Qihang Ma, Shengyu Li, Jie Tang, Dingkang Yang, null Chenshaodong, Yingyi Zhang, Chao Feng, Ran Jiao
| Challenge: | Multi-modal keyphrase prediction (MMKP) aims to produce concise, informative phrases that capture the essence of cross-modal inputs. |
| Approach: | They propose to use vision-language models to generate conclusive phrases using multiple modalities of input information. |
| Outcome: | The proposed methods outperform existing methods on absence and unseen scenarios and overestimate model capability due to overlap in training tests. |
Guiding Variational Response Generator to Exploit Persona (2020.acl-main)
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Bowen Wu, MengYuan Li, Zongsheng Wang, Yifu Chen, Derek F. Wong, Qihang Feng, Junhong Huang, Baoxun Wang
| Challenge: | Neural Response Generators (NRGs) use persona information of users to perform personalized conversations . current studies focus on incorporating explicit meta-data of user profiles or character descriptions to generate persona-aware responses. |
| Approach: | They propose to use persona information of users in Neural Response Generators to perform personalized conversations. |
| Outcome: | The proposed method improves persona-aware response generation and the metrics are reasonable to evaluate them. |
Towards Non-task-specific Distillation of BERT via Sentence Representation Approximation (2020.aacl-main)
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| Challenge: | Existing methods for transferring knowledge from BERT into a model with large parameters are not efficient due to their large-scale and high computational cost. |
| Approach: | They propose a sentence representation approximating oriented distillation framework that can distill pre-trained BERT into a simple LSTM based model without specifying tasks. |
| Outcome: | The proposed model outperforms other distillation methods and larger models on multiple NLP tasks with efficiency well-improved. |
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior (D19-58)
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| Challenge: | Recent studies indicate that the current machine reading comprehension systems suffer from weak robustness against adversarial samples. |
| Approach: | They propose to take sentence syntax as the leverage in the answer predicting process and exploit the syntactic elements of a question to improve the generalization and robustness of MRC models. |
| Outcome: | The proposed method improves generalization and robustness against adversarial samples, with performance well-maintained. |