Analyzing facts and opinions in Nepali subjective texts

Regmi, Santosh; Bal, Bal Krishna; Kultsova, Marina

doi:10.1109/iisa.2017.8316445

Cited by 3 publications

(2 citation statements)

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“…179 for positive and 205 for negative, and MNB outperformed other methods. Over few years, several research (Piryani et al, 2020;Regmi et al, 2017;Tamrakar et al, 2020) have been conducted using traditional machine learning and deep learning models for Nepali sentiment classification.…”

Section: Text Classificationmentioning

confidence: 99%

“…A wide range of traditional machine learning algorithms and deep learning models have been trained to perform text classification tasks in Nepali, mostly news group classification (Basnet & Timalsina, 2018;Kafle, Sharma, Subedi, & Timalsina, 2016;Koirala & Niraula, 2021;T.B. Shahi & Pant, 2018;Singh, 2018;Subba, Paudel, & Shahi, 2019;Thakur & Singh, 2014;Wagle & Thapa, 2021) and sentiment analysis (Piryani, Piryani, Singh, & Pinto, 2020;Regmi, Bal, & Kultsova, 2017;T. Shahi, Sitaula, & Paudel, 2022;Sitaula, Basnet, Mainali, & Shahi, 2021;Tamrakar, Bal, & Thapa, 2020;Thapa & Bal, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Comparative Evaluation of Transformer-Based Nepali Language Models

Tamrakar

Silpasuwanchai

2022

Preprint

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Large pre-trained transformer models using self-supervised learning have achieved state-of-the-art performances in various NLP tasks. However, for low-resource language like Nepali, pre-training of monolingual models remains a problem due to lack of training data and well-designed and balanced benchmark datasets. Furthermore, several multilingual pre-trained models such as mBERT and XLM-RoBERTa have been released, but their performance remains unknown for Nepali language. We compared Nepali monolingual pre-trained transformer models with multilingual models to determine their performance using a Nepali text classification dataset as a downstream task based on different number of classes and data sizes, taking machine learning (ML) and deep learning (DL) algorithms as baselines. Under-representation of Nepali language in mBERT resulted in overall poor performance, but, XLM-RoBERTa, which has a larger vocabulary size, produced state-of-the-art performance which is relatively similar to that of Nepali DistilBERT and DeBERTa, which outperformed all of the baseline algorithms. Bi-LSTM and SVM from the baselines also performed very well in variety of settings. Moreover, to assess the cross-language knowledge transfer for the cases when mono-lingual models are not available, we also evaluated HindiRoBERTa, a monolingual Indian language model on Nepali text dataset. This research mainly contributes to the Nepali NLP community by creation of news classification dataset with 20 classes, with over 200,000 articles and performance evaluation of various pre-trained monolingual Nepali transformers with multilingual transformers, DL and ML algorithms.

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Section: Text Classificationmentioning

confidence: 99%