Indonesia's linguistic landscape is remarkably diverse, encompassing over 700
languages and dialects, making it one of the world's most linguistically rich
nations. This diversity, coupled with the widespread practice of code-switching
and the presence of low-resource regional languages, presents unique challenges
for modern pre-trained language models. In response to these challenges, we
developed NusaBERT, building upon IndoBERT by incorporating vocabulary
expansion and leveraging a diverse multilingual corpus that includes regional
languages and dialects. Through rigorous evaluation across a range of
benchmarks, NusaBERT demonstrates state-of-the-art performance in tasks
involving multiple languages of Indonesia, paving the way for future natural
language understanding research for under-represented languages.

NusaBERT 是一种基于 IndoBERT 的模型，通过词汇扩展和多样化的多语种语料库，解决了印度尼西亚多语种和方言的自然语言理解问题，取得了最先进的成果，为研究少数语言奠定了基础。

NusaBERT: 将 IndoBERT 教成多语言和多文化

NusaBERT: Teaching IndoBERT to be Multilingual and Multicultural

Linguistic landscape is an important field in sociolinguistic research. Eye
tracking technology is a common technology in psychological research. There are
few cases of using eye movement to study linguistic landscape. This paper uses
eye tracking technology to study the actual fixation of the linguistic
landscape and finds that in the two dimensions of fixation time and fixation
times, the fixation of native Chinese speakers to the linguistic landscape is
higher than that of the general landscape. This paper argues that this
phenomenon is due to the higher information density of linguistic landscapes.
At the same time, the article also discusses other possible reasons for this
phenomenon.

通过使用眼动追踪技术研究了语言景观的实际注视，本文发现在注视时间和注视次数这两个维度上，中国本族语言使用者对语言景观的注视明显高于一般景观，尤其是由于语言景观的信息密度较高所引起的。同时，本文还讨论了其他可能的原因。

利用眼动追踪研究中文母语者对语言景观图像的注意力

Using eye tracking to investigate what native Chinese speakers notice  about linguistic landscape images

As voice assistants cement their place in our technologically advanced
society, there remains a need to cater to the diverse linguistic landscape,
including colloquial forms of low-resource languages. Our study introduces the
first-ever comprehensive dataset for intent detection and slot filling in
formal Bangla, colloquial Bangla, and Sylheti languages, totaling 984 samples
across 10 unique intents. Our analysis reveals the robustness of large language
models for tackling downstream tasks with inadequate data. The GPT-3.5 model
achieves an impressive F1 score of 0.94 in intent detection and 0.51 in slot
filling for colloquial Bangla.

我们的研究通过引入首个全面的数据集，针对正式孟加拉语、口头孟加拉语和锡尔赫蒂语中的意图检测和槽填充，总计 984 个样本，涵盖 10 个独特的意图。分析表明，大型语言模型在处理数据不足的下游任务方面具有鲁棒性。GPT-3.5 模型在口头孟加拉语的意图检测得分为 0.94，在槽填充方面得分为 0.51。