Lexical complexity prediction (LCP) is the task of predicting the complexity
of words in a text on a continuous scale. It plays a vital role in simplifying
or annotating complex words to assist readers. To study lexical complexity in
Japanese, we construct the first Japanese LCP dataset. Our dataset provides
separate complexity scores for Chinese/Korean annotators and others to address
the readers' L1-specific needs. In the baseline experiment, we demonstrate the
effectiveness of a BERT-based system for Japanese LCP.

构建了针对日语的第一个 LCP 数据集，并通过 BERT 模型在基准实验中证明了其在日语 LCP 任务中的有效性，同时提供了适合中文 / 韩文标注者和其他读者的分离复杂度得分以满足读者的母语特定需求。

非母语读者的日语词汇复杂性：一个新的数据集

Japanese Lexical Complexity for Non-Native Readers: A New Dataset

A positive phrase or a sentence with an underlying negative motive is usually
defined as sarcasm that is widely used in today's social media platforms such
as Facebook, Twitter, Reddit, etc. In recent times active users in social media
platforms are increasing dramatically which raises the need for an automated
NLP-based system that can be utilized in various tasks such as determining
market demand, sentiment analysis, threat detection, etc. However, since
sarcasm usually implies the opposite meaning and its detection is frequently a
challenging issue, data meaning extraction through an NLP-based model becomes
more complicated. As a result, there has been a lot of study on sarcasm
detection in English over the past several years, and there's been a noticeable
improvement and yet sarcasm detection in the Bangla language's state remains
the same. In this article, we present a BERT-based system that can achieve
99.60\% while the utilized traditional machine learning algorithms are only
capable of achieving 89.93\%. Additionally, we have employed Local
Interpretable Model-Agnostic Explanations that introduce explainability to our
system. Moreover, we have utilized a newly collected bangla sarcasm dataset,
BanglaSarc that was constructed specifically for the evaluation of this study.
This dataset consists of fresh records of sarcastic and non-sarcastic comments,
the majority of which are acquired from Facebook and YouTube comment sections.

本文提出了一个基于 BERT 的系统，可以在 Bangla sarcasm dataset 上实现 99.60％的检测准确率，在 NLP 和机器学习领域中实现了与传统算法 89.93％的比较。还引入了 Local Interpretable Model-Agnostic Explanations 来实现可解释性。

使用 BERT 和可解释 AI 进行可解释的孟加拉挖苦检测

Interpretable Bangla Sarcasm Detection using BERT and Explainable AI

Language identification is the task of automatically determining the identity
of a language conveyed by a spoken segment. It has a profound impact on the
multilingual interoperability of an intelligent speech system. Despite language
identification attaining high accuracy on medium or long utterances(>3s), the
performance on short utterances (<=1s) is still far from satisfactory. We
propose a BERT-based language identification system (BERT-LID) to improve
language identification performance, especially on short-duration speech
segments. We extend the original BERT model by taking the phonetic
posteriorgrams (PPG) derived from the front-end phone recognizer as input. Then
we deployed the optimal deep classifier followed by it for language
identification. Our BERT-LID model can improve the baseline accuracy by about
6.5% on long-segment identification and 19.9% on short-segment identification,
demonstrating our BERT-LID's effectiveness to language identification.

我们提出了一种基于 BERT 的语言识别系统，通过提取从前端语音识别器导出的语音学后向图（PPG）作为输入，可以提高较短语音段的语言识别表现，该模型可以提高长语音段识别的基准准确率约 6.5％，提高短语音段识别的基准准确率约 19.9％，表明 BERT-LID 在语言识别方面是有效的。