Papers by Yameng Li
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. |
Word-level Prefix/Suffix Sense Detection: A Case Study on Negation Sense with Few-shot Learning (2023.findings-acl)
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| Challenge: | Morphological analysis is an important research issue in natural language processing . prefixes/suffixes are sometimes ambiguous, causing difficulty in detecting negation sense . |
| Approach: | They propose a context-free morphological analysis task that deals with negation sense . they propose morphology task that uses input-augmentation prompts to train a model . |
| Outcome: | The proposed approach is effective in detecting negation senses in a corpus of prefixes/suffixes . Empirical studies show that the proposed approach works in context-free mode . |