Summary of Farssibert: a Novel Transformer-based Model For Semantic Similarity Measurement Of Persian Social Networks Informal Texts, by Seyed Mojtaba Sadjadi et al.
FarSSiBERT: A Novel Transformer-based Model for Semantic Similarity Measurement of Persian Social Networks Informal Texts
by Seyed Mojtaba Sadjadi, Zeinab Rajabi, Leila Rabiei, Mohammad-Shahram Moin
First submitted to arxiv on: 27 Jul 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 This paper introduces a transformer-based model to measure semantic similarity between Persian informal short texts from social networks. The proposed model, called FarSSiBERT, is pre-trained on approximately 104 million Persian informal short texts from social networks, making it one of a kind in the Persian language. The model outperforms ParsBERT, laBSE, and multilingual BERT in the Pearson and Spearman’s coefficient criteria. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to compare two pieces of text written in the Persian language. Right now, there aren’t many good ways to do this because previous methods didn’t work well for informal texts like those found on social networks. The researchers created a new model called FarSSiBERT that can help with this task. They also made a special dataset for Persian text that’s useful for training the model. |
Keywords
» Artificial intelligence » Bert » Transformer
