Papers by Minghui Zhai
UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to reduce word embedding parameters ignore semantic information . existing methods do not consider semantic information, allowing for performance degradation . |
| Approach: | They propose a method that leverages semantic similarity with weight sharing to reduce dimensionality of word embeddings. |
| Outcome: | The proposed method reduces word embedding parameters by more than 11x on a standard English-German dataset. |
Domain-aware and Co-adaptive Feature Transformation for Domain Adaption Few-shot Relation Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to relation extraction focus on the source domain, which makes it difficult to accurately transfer useful knowledge to the target domain. |
| Approach: | They propose a domain-aware and co-adaptive feature transformation approach to address these issues by leveraging the target domain distribution features to guide the domain-based feature transformations. |
| Outcome: | The proposed method outperforms existing models and achieves state-of-the-art performance on a benchmark dataset. |