Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results.

RTE is a significant problem and is a reasonably active research community. The proposed research works on the approach to this problem are pretty diverse with many different directions. For Vietnamese, the RTE problem is moderately new, but this problem plays a vital role in natural language understanding systems. Currently, methods to solve this problem based on contextual word representation learning models have given outstanding results. However, Vietnamese is a semantically rich language. Therefore, in this paper, we want to present an experiment combining semantic word representation through the SRL task with context representation of BERT relative models for the RTE problem. The experimental results give conclusions about the influence and role of semantic representation on Vietnamese in understanding natural language. The experimental results show that the semantic-aware contextual representation model has about 1% higher performance than the model that does not incorporate semantic representation. In addition, the effects on the data domain in Vietnamese are also higher than those in English. This result also shows the positive influence of SRL on RTE problem in Vietnamese.

To the best of our knowledge, this paper made the first attempt to answer whether word segmentation is necessary for Vietnamese sentiment classification. To do this, we presented five pre-trained monolingual S4- based language models for Vietnamese, including one model without word segmentation, and four models using RDRsegmenter, uitnlp, pyvi, or underthesea toolkits in the pre-processing data phase. According to comprehensive experimental results on two corpora, including the VLSP2016-SA corpus of technical article reviews from the news and social media and the UIT-VSFC corpus of the educational survey, we have two suggestions. Firstly, using traditional classifiers like Naive Bayes or Support Vector Machines, word segmentation maybe not be necessary for the Vietnamese sentiment classification corpus, which comes from the social domain. Secondly, word segmentation is necessary for Vietnamese sentiment classification when word segmentation is used before using the BPE method and feeding into the deep learning model. In this way, the RDRsegmenter is the stable toolkit for word segmentation among the uitnlp, pyvi, and underthesea toolkits.

文本分类是具有各种有趣应用程序的典型自然语言处理或计算语言学任务。随着社交媒体平台上的用户数量的增加，数据加速促进了有关社交媒体文本分类（SMTC）或社交媒体文本挖掘的新兴研究。与英语相比，越南人是低资源语言之一，仍然没有集中精力并彻底利用。受胶水成功的启发，我们介绍了社交媒体文本分类评估（SMTCE）基准，作为各种SMTC任务的数据集和模型的集合。借助拟议的基准，我们实施和分析了各种基于BERT的模型（Mbert，XLM-R和Distilmbert）和基于单语的BERT模型（Phobert，Vibert，Vibert，Velectra和Vibert4news）的有效性SMTCE基准。单语模型优于多语言模型，并实现所有文本分类任务的最新结果。它提供了基于基准的多语言和单语言模型的客观评估，该模型将使越南语言中有关贝尔特兰的未来研究有利。

近年来，问题回答（QA）系统引起了爆炸性的关注。但是，越南语中的质量检查任务没有很多数据集。值得注意的是，医疗域中大多没有数据集。因此，我们为回答数据集（VIHealthQA）建立了一个越南医疗保健问题，其中包括10,015个问题 - 答案段落对，以实现这项任务，其中在享有盛名的健康网站上问了来自健康利益的用户的问题，并在享有资格的专家中得到了答案。本文提出了一个基于句子 - 伯特（Sbert）的两阶段质量检查系统，使用多个负损失（MNR）损失与BM25结合在一起。然后，我们使用许多单词范围的模型进行多种实验，以评估系统的性能。通过获得的结果，该系统的性能比传统方法更好。

问题回答（QA）是信息检索和信息提取领域内的一项自然理解任务，由于基于机器阅读理解的模型的强劲发展，近年来，近年来，近年来的计算语言学和人工智能研究社区引起了很多关注。基于读者的质量检查系统是一种高级搜索引擎，可以使用机器阅读理解（MRC）技术在开放域或特定领域特定文本中找到正确的查询或问题的答案。 MRC和QA系统中的数据资源和机器学习方法的大多数进步尤其是在两种资源丰富的语言中显着开发的，例如英语和中文。像越南人这样的低资源语言见证了关于质量检查系统的稀缺研究。本文介绍了XLMRQA，这是第一个在基于Wikipedia的文本知识源（使用UIT-Viquad语料库）上使用基于变压器的读取器的越南质量检查系统，使用深​​层神经网络模型优于DRQA和BERTSERINI，优于两个可靠的QA系统分别为24.46％和6.28％。从三个系统获得的结果中，我们分析了问题类型对质量检查系统性能的影响。

在计算语言学和自然语言处理的应用方面，中文字分割和语音标记是必要的任务。许多重新搜索者仍然辩论对深度学习时代中汉语词组和演讲的一部分。尽管如此，解决歧义并检测到未知词是挑战这一领域的问题。以前关于联合中文分割和语音标记的研究主要遵循关注的基于角色的标记模型，专注于模拟n-gram功能。与以前的作品不同，我们提出了一个名为SpanseGtag的神经模型，用于联合中文字分割和跨度标记之后的语音标记，其中每个n克是单词和词语标签的概率是主要的问题。我们在连续字符的左边和右边界表示的左边和右边界表示中使用双重边界表示来模拟n-gram。我们的实验表明，我们的BERT基模型SPANSEGTAG在CTB5，CTB6和UD上实现了竞争性能，或者使用BERT或ZEN编码器的当前最先进的方法对CTB7和CTB9基准数据集进行了显着改进。

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

Video understanding is a growing field and a subject of intense research, which includes many interesting tasks to understanding both spatial and temporal information, e.g., action detection, action recognition, video captioning, video retrieval. One of the most challenging problems in video understanding is dealing with feature extraction, i.e. extract contextual visual representation from given untrimmed video due to the long and complicated temporal structure of unconstrained videos. Different from existing approaches, which apply a pre-trained backbone network as a black-box to extract visual representation, our approach aims to extract the most contextual information with an explainable mechanism. As we observed, humans typically perceive a video through the interactions between three main factors, i.e., the actors, the relevant objects, and the surrounding environment. Therefore, it is very crucial to design a contextual explainable video representation extraction that can capture each of such factors and model the relationships between them. In this paper, we discuss approaches, that incorporate the human perception process into modeling actors, objects, and the environment. We choose video paragraph captioning and temporal action detection to illustrate the effectiveness of human perception based-contextual representation in video understanding. Source code is publicly available at https://github.com/UARK-AICV/Video_Representation.

自我训练的人群计数尚未得到专心探索，尽管这是计算机视觉中的重要挑战之一。实际上，完全监督的方法通常需要大量的手动注释资源。为了应对这一挑战，这项工作引入了一种新的方法，以利用现有的数据集，以地面真理来在人群计数中对未标记的数据集（称为域名适应）产生更强大的预测。尽管网络接受了标记的数据训练，但培训过程中还添加了来自目标域的标签的样品。在此过程中，除了平行设计的对抗训练过程外，还计算和最小化熵图。在shanghaitech，UCF_CC_50和UCF-QNRF数据集上进行的实验证明，在跨域设置中，我们的方法对我们的方法进行了更广泛的改进。