Summary of Entity-aware Biaffine Attention Model For Improved Constituent Parsing with Reduced Entity Violations, by Xinyi Bai
Entity-Aware Biaffine Attention Model for Improved Constituent Parsing with Reduced Entity Violations
by Xinyi Bai
First submitted to arxiv on: 1 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 entity-aware biaffine attention model is proposed for constituency parsing, a task that involves breaking down sentences into sub-phrases or constituents. The model addresses the issue of entity violations, where an entity fails to form a complete sub-tree in the resulting parsing tree. The entity information is incorporated into the biaffine attention mechanism using additional entity role vectors for potential phrases, which enhances the parsing accuracy. The proposed model achieves state-of-the-art performance on three popular datasets-ONTONOTES, PTB, and CTB-while maintaining high precision, recall, and F1-scores comparable to existing models. The effectiveness of the model is further evaluated in downstream tasks such as sentence sentiment analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new machine learning model helps computers better understand sentences by breaking them down into smaller parts. This is called constituency parsing. Sometimes, the model might not get it right and miss important details. To fix this, researchers created a special attention mechanism that uses extra information about what’s happening in the sentence. This makes the model much more accurate. The new approach was tested on three different sets of sentences and did very well. It even worked well when used for other tasks like figuring out if a sentence is positive or negative. |
Keywords
» Artificial intelligence » Attention » Machine learning » Parsing » Precision » Recall
