We introduce an approach for the answer-aware question generation problem. Instead of only relying on the capability of strong pre-trained language models, we observe that the information of answers and questions can be found in some relevant sentences in the context. Based on that, we design a model which includes two modules: a selector and a generator. The selector forces the model to more focus on relevant sentences regarding an answer to provide implicit local information. The generator generates questions by implicitly combining local information from the selector and global information from the whole context encoded by the encoder. The model is trained jointly to take advantage of latent interactions between the two modules. Experimental results on two benchmark datasets show that our model is better than strong pre-trained models for the question generation task. The code is also available (shorturl.at/lV567).

成功的人工智能系统通常需要大量标记的数据来从文档图像中提取信息。在本文中，我们研究了改善人工智能系统在理解文档图像中的性能的问题，尤其是在培训数据受到限制的情况下。我们通过使用加强学习提出一种新颖的填充方法来解决问题。我们的方法将信息提取模型视为策略网络，并使用策略梯度培训来更新模型，以最大程度地提高补充传统跨凝结损失的综合奖励功能。我们使用标签和专家反馈在四个数据集上进行的实验表明，我们的填充机制始终提高最先进的信息提取器的性能，尤其是在小型培训数据制度中。

本文介绍了一种称为JET（\ textbf {j} oint Learning Token \ textbf {e} Xtraction和\ textbf {t} ext Exenation）的模型（IUR）。与仅在提取或抽象数据集上工作的先前研究不同，我们设计了一个简单但有效的模型，适用于IUR的两种情况。我们的设计模拟了IUR的性质，在上下文中省略了令牌有助于恢复。由此，我们构建了一个识别省略令牌的选择器。为了支持选择器，我们设计了两种标签创建方法（软标签和硬标签），它们可以在没有注释数据的情况下使用省略的令牌。恢复是通过在关节学习的帮助者的帮助下使用发电机来完成的。在提取和抽象方案中的四个基准数据集上的有希望的结果表明，我们的模型比富裕和有限的培训数据设置中的验证的T5和非生成语言模型方法。 //github.com/shumpei19/jet}}}

法律文本的自动摘要是一个重要的且仍然是一个具有挑战性的任务，因为法律文件往往是长期的，并且具有不寻常的结构和风格。深层模型的最近进步培训结束于终端以可分辨率的损失总结自然文本，但在适用于合法领域时，它们会显示有限的结果。在本文中，我们建议使用强化学习来培养当前的深度摘要模型，以提高其对法律领域的表现。为此，我们采用了近端政策优化方法，并引入了新的奖励函数，鼓励一代满足词汇和语义标准的候选摘要。我们将我们的方法应用于培训不同的摘要骨架，并在3个公共法律数据集中遵守一致而显着的性能增益。

Here, we demonstrate how machine learning enables the prediction of comonomers reactivity ratios based on the molecular structure of monomers. We combined multi-task learning, multi-inputs, and Graph Attention Network to build a model capable of predicting reactivity ratios based on the monomers chemical structures.

Modern deep neural networks have achieved superhuman performance in tasks from image classification to game play. Surprisingly, these various complex systems with massive amounts of parameters exhibit the same remarkable structural properties in their last-layer features and classifiers across canonical datasets. This phenomenon is known as "Neural Collapse," and it was discovered empirically by Papyan et al. \cite{Papyan20}. Recent papers have theoretically shown the global solutions to the training network problem under a simplified "unconstrained feature model" exhibiting this phenomenon. We take a step further and prove the Neural Collapse occurrence for deep linear network for the popular mean squared error (MSE) and cross entropy (CE) loss. Furthermore, we extend our research to imbalanced data for MSE loss and present the first geometric analysis for Neural Collapse under this setting.

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.

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.