Aspect-based sentiment analysis is a method in natural language processing
aimed at identifying and understanding sentiments related to specific aspects
of an entity. Aspects are words or phrases that represent an aspect or
attribute of a particular entity. Previous research has utilized generative
pre-trained language models to perform aspect-based sentiment analysis.
LEGO-ABSA is one framework that has successfully employed generative
pre-trained language models in aspect-based sentiment analysis, particularly in
English. LEGO-ABSA uses a multitask learning and prompting approach to enhance
model performance. However, the application of this approach has not been done
in the context of Bahasa Indonesia. Therefore, this research aims to implement
the multitask learning and prompting approach in aspect-based sentiment
analysis for Bahasa Indonesia using generative pre-trained language models. In
this study, the Indo LEGO-ABSA model is developed, which is an aspect-based
sentiment analysis model utilizing generative pre-trained language models and
trained with multitask learning and prompting. Indo LEGO-ABSA is trained with a
hotel domain dataset in the Indonesian language. The obtained results include
an f1-score of 79.55% for the Aspect Sentiment Triplet Extraction task, 86.09%
for Unified Aspect-based Sentiment Analysis, 79.85% for Aspect Opinion Pair
Extraction, 87.45% for Aspect Term Extraction, and 88.09% for Opinion Term
Extraction. Indo LEGO-ABSA adopts the LEGO-ABSA framework that employs the T5
model, specifically mT5, by applying multitask learning to train all tasks
within aspect-based sentiment analysis.

本研究通过应用多任务学习和提示法，利用生成式预训练语言模型在 Bahasa Indonesia 上实施基于方面的情感分析，开发了 Indo LEGO-ABSA 模型，该模型在印尼语言的酒店领域数据集上训练，得到了高准确率的结果。

印度尼西亚 LEGO-ABSA：多任务生成式基于方面的情感分析

Indo LEGO-ABSA: A Multitask Generative Aspect Based Sentiment Analysis  for Indonesian Language

Fake news is fake material in a news media format but is not processed
properly by news agencies. The fake material can provoke or defame significant
entities or individuals or potentially even for the personal interests of the
creators, causing problems for society. Distinguishing fake news and real news
is challenging due to limited of domain knowledge and time constraints.
According to the survey, the top three areas most exposed to hoaxes and
misinformation by residents are in Banten, DKI Jakarta and West Java. The model
of transformers is referring to an approach in the field of artificial
intelligence (AI) in natural language processing utilizing the deep learning
architectures. Transformers exercise a powerful attention mechanism to process
text in parallel and produce rich and contextual word representations. A
previous study indicates a superior performance of a transformer model known as
BERT over and above non transformer approach. However, some studies suggest the
performance can be improved with the use of improved BERT models known as
ALBERT and RoBERTa. However, the modified BERT models are not well explored for
detecting fake news in Bahasa Indonesia. In this research, we explore those
transformer models and found that ALBERT outperformed other models with 87.6%
accuracy, 86.9% precision, 86.9% F1-score, and 174.5 run-time (s/epoch)
respectively. Source code available at:
this https URL

该研究探索了 transformer 模型，发现 ALBERT 在检测印尼语假新闻方面表现最佳

基于 Transformer 模型（BERT，ALBERT 和 RoBERTa）在假新闻检测中的性能分析

Performance Analysis of Transformer Based Models (BERT, ALBERT and  RoBERTa) in Fake News Detection

This paper provides an overall introduction of our Automatic Speech
Recognition (ASR) systems for Southeast Asian languages. As not much existing
work has been carried out on such regional languages, a few difficulties should
be addressed before building the systems: limitation on speech and text
resources, lack of linguistic knowledge, etc. This work takes Bahasa Indonesia
and Thai as examples to illustrate the strategies of collecting various
resources required for building ASR systems.

介绍我们为东南亚语言开发的自动语音识别系统，以印尼语和泰语为例解释了收集语音和文本资源所需的各种策略。

面向东南亚语言的云自动语音识别系统

Cloud-based Automatic Speech Recognition Systems for Southeast Asian Languages

Smoothing is one technique to overcome data sparsity in statistical language
model. Although in its mathematical definition there is no explicit dependency
upon specific natural language, different natures of natural languages result
in different effects of smoothing techniques. This is true for Russian language
as shown by Whittaker (1998). In this paper, We compared Modified Kneser-Ney
and Witten-Bell smoothing techniques in statistical language model of Bahasa
Indonesia. We used train sets of totally 22M words that we extracted from
Indonesian version of Wikipedia. As far as we know, this is the largest train
set used to build statistical language model for Bahasa Indonesia. The
experiments with 3-gram, 5-gram, and 7-gram showed that Modified Kneser-Ney
consistently outperforms Witten-Bell smoothing technique in term of perplexity
values. It is interesting to note that our experiments showed 5-gram model for
Modified Kneser-Ney smoothing technique outperforms that of 7-gram. Meanwhile,
Witten-Bell smoothing is consistently improving over the increase of n-gram
order.

本研究使用印尼语维基百科的训练集，比较了 Modified Kneser-Ney 和 Witten-Bell 平滑技术在统计语言模型中的性能表现，实验结果显示 Modified Kneser-Ney 在困惑度值方面表现更好。