Papers by Yameng Huang
CULG: Commercial Universal Language Generation (2022.naacl-industry)
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| Challenge: | Pre-trained language models have improved performance for many NLP tasks in finance and healthcare. |
| Approach: | They propose a large-scale commercial universal language generation model which is pre-trained on a corpus drawn from 10 markets across 7 languages. |
| Outcome: | The proposed model outperforms other models on commercial generation tasks and on other markets, languages, and tasks. |
An Enhanced Knowledge Injection Model for Commonsense Generation (2020.coling-main)
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Zhihao Fan, Yeyun Gong, Zhongyu Wei, Siyuan Wang, Yameng Huang, Jian Jiao, Xuanjing Huang, Nan Duan, Ruofei Zhang
| Challenge: | a recent study shows that digging the relationship of concepts from scratch is non-trivial for commonsense generation tasks. |
| Approach: | They use a retrieve-and-edit framework to retrieve a prototype with these concepts . they use qt and qq to generate commonsense questions at scale . |
| Outcome: | The proposed method significantly improves the performance on commonsense generation tasks. |