Summary of Distilling Monolingual and Crosslingual Word-in-context Representations, by Yuki Arase and Tomoyuki Kajiwara
Distilling Monolingual and Crosslingual Word-in-Context Representations
by Yuki Arase, Tomoyuki Kajiwara
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is introduced to distill word meaning representations from pre-trained masked language models, applicable in both monolingual and cross-lingual settings. This method learns to combine hidden layer outputs using self-attention without requiring human-annotated corpora or updating the pre-trained model parameters. The proposed auto-encoder based training utilizes an automatically generated corpus. Extensive experiments were conducted on various benchmark tasks, demonstrating competitive performance for context-aware lexical semantic tasks and outperforming previous methods for semantic textual similarity estimation in monolingual settings. Cross-lingual results show significant improvements in word representations for multilingual pre-trained models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand words is developed by using a special type of artificial intelligence model called a masked language model. This method can work with words from different languages and doesn’t need human help to train it. The model combines information from different layers to create a better understanding of word meanings. It was tested on various tasks and showed great results, especially when comparing similar texts in the same language. When working with texts in different languages, this approach improved how well models could understand words. |
Keywords
» Artificial intelligence » Encoder » Masked language model » Self attention
