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TookaBERT: A Step Forward for Persian NLU

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arxiv 2407.16382 v1 pith:3XTCEGUB submitted 2024-07-23 cs.CL

MohammadAli SadraeiJavaheri , Ali Moghaddaszadeh , Milad Molazadeh , Fariba Naeiji , Farnaz Aghababaloo , Hamideh Rafiee , Zahra Amirmahani , Tohid Abedini
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Fatemeh Zahra Sheikhi Amirmohammad Salehoof
This is my paper · ORCID
classification cs.CL
keywords modelspersianbertlanguagenaturaltasksacrossadvancements
verification ladder T0 review T1 audit T2 compute T3 formal
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The field of natural language processing (NLP) has seen remarkable advancements, thanks to the power of deep learning and foundation models. Language models, and specifically BERT, have been key players in this progress. In this study, we trained and introduced two new BERT models using Persian data. We put our models to the test, comparing them to seven existing models across 14 diverse Persian natural language understanding (NLU) tasks. The results speak for themselves: our larger model outperforms the competition, showing an average improvement of at least +2.8 points. This highlights the effectiveness and potential of our new BERT models for Persian NLU tasks.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Advancing Retrieval-Augmented Generation for Persian: Development of Language Models, Comprehensive Benchmarks, and Best Practices for Optimization

    cs.CL 2025-01 reject novelty 5.0 of 10

    Persian-focused embedding and language models are introduced and benchmarked for RAG, but evaluation inconsistencies prevent the main performance claims from being accepted.

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