The task of Spell Correction(SC) in low-resource languages presents a
significant challenge due to the availability of only a limited corpus of data
and no annotated spelling correction datasets. To tackle these challenges a
small-scale word-based transformer LM is trained to provide the SC model with
contextual understanding. Further, the probabilistic error rules are extracted
from the corpus in an unsupervised way to model the tendency of error
happening(error model). Then the combination of LM and error model is used to
develop the SC model through the well-known noisy channel framework. The
effectiveness of this approach is demonstrated through experiments on the
Nepali language where there is access to just an unprocessed corpus of textual
data.

通过训练小规模的基于词的 Transformer 语言模型，提取语料库中的概率性错误规则，结合语言模型和错误模型，通过嘈杂信道框架开发拼写校正模型，实验验证了该方法在尼泊尔语中的有效性。

低资源环境下基于语言模型的上下文拼写纠错

Contextual Spelling Correction with Language Model for Low-resource  Setting

Billions of people across the globe have been using social media platforms in
their local languages to voice their opinions about the various topics related
to the COVID-19 pandemic. Several organizations, including the World Health
Organization, have developed automated social media analysis tools that
classify COVID-19-related tweets into various topics. However, these tools that
help combat the pandemic are limited to very few languages, making several
countries unable to take their benefit. While multi-lingual or low-resource
language-specific tools are being developed, they still need to expand their
coverage, such as for the Nepali language. In this paper, we identify the eight
most common COVID-19 discussion topics among the Twitter community using the
Nepali language, set up an online platform to automatically gather Nepali
tweets containing the COVID-19-related keywords, classify the tweets into the
eight topics, and visualize the results across the period in a web-based
dashboard. We compare the performance of two state-of-the-art multi-lingual
language models for Nepali tweet classification, one generic (mBERT) and the
other Nepali language family-specific model (MuRIL). Our results show that the
models' relative performance depends on the data size, with MuRIL doing better
for a larger dataset. The annotated data, models, and the web-based dashboard
are open-sourced at this https URL.

本研究基于 Nepali 语言使用自动化的社交媒体分析工具，将 COVID-19 相关的推文自动分类为八个主题，并使用网络平台呈现结果，该研究比较了 Nepali 的两个最新多语言语言模型（mBERT 和 MuRIL）的表现。结果表明，MuRIL 在大数据集下表现更好。

在低资源环境中分类 COVID-19 相关的尼泊尔推特

COVID-19-related Nepali Tweets Classification in a Low Resource Setting

Named Entity Recognition have been studied for different languages like
English, German, Spanish and many others but no study have focused on Nepali
language. In this paper we propose a neural based Nepali NER using latest
state-of-the-art architecture based on grapheme-level which doesn't require any
hand-crafted features and no data pre-processing. Our novel neural based model
gained relative improvement of 33% to 50% compared to feature based SVM model
and up to 10% improvement over state-of-the-art neural based model developed
for languages beside Nepali.

本文提出了一种基于神经网络的尼泊尔语命名实体识别模型，采用最新的基于字形的架构，无需手工制作特征和数据预处理，相对于基于特征的 SVM 模型以及针对尼泊尔语以外语言开发的最新神经模型，获得了 33% 至 50% 的相对改进和高达 10% 的改进。