Summary of Persian Pronoun Resolution: Leveraging Neural Networks and Language Models, by Hassan Haji Mohammadi et al.
Persian Pronoun Resolution: Leveraging Neural Networks and Language Models
by Hassan Haji Mohammadi, Alireza Talebpour, Ahmad Mahmoudi Aznaveh, Samaneh Yazdani
First submitted to arxiv on: 17 May 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 The proposed end-to-end neural network system for Persian pronoun resolution leverages pre-trained Transformer models like ParsBERT to jointly optimize mention detection and antecedent linking. The system achieves a 3.37 F1 score improvement over the previous state-of-the-art system on the Mehr corpus, demonstrating the effectiveness of combining neural networks with linguistic models in this under-explored area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand what things are saying about each other has been developed. This is important because it helps us identify which words are talking about the same thing. Right now, computers have trouble doing this, especially when trying to figure out what pronouns (words that replace nouns) mean. The current approach separates two tasks: finding what’s being talked about and figuring out who’s saying it. But this new method combines these tasks in one step, making it better at understanding text. |
Keywords
» Artificial intelligence » F1 score » Neural network » Transformer
