Estratégias e constrangimentos na aprendizagem da oralidade da língua portuguesa por estudantes chineses

A Língua Portuguesa é um dos mais importantes idiomas do mundo (Lessa, 2010) e cada vez mais pessoas a aprendem. O alvo mais importante para aprender uma língua será a comunicação oral com os outros e, por isso, a oralidade uma parte relevante na aprendizagem de língua. A língua portuguesa também é uma das línguas mais difíceis de aprender, pelo que a aprendizagem da oralidade da língua portuguesa tornou-se um tema amplamente discutido e analisado na área da aprendizagem de línguas estrangeiras. O desenvolvimento da língua portuguesa na China tem pouco tempo, não há muitas pessoas que a falem, a capacidade de oralidade nos estudantes chineses é fraca. Por isso, o estudo das suas estratégias, fatores e medidas tem um importante significado. O presente estudo tem como objetivo analisar o status de aprendizagem da oralidade da língua portuguesa, as dificuldades e, especialmente, os constrangimentos e as estratégias eficazes, com base nas teorias relacionadas com a aquisição de segunda língua, concretamente na aquisição de oralidade. Através da utilização de um questionário para coletar informações, analisamos e discutimos os dados e resumimos as conclusões com significado prático. Aplicámos o questionário a 72 participantes provenientes de diferentes partes da China e as suas opiniões permitiram-nos tecer considerações sobre como melhorar e ajudar os estudantes na aprendizagem da oralidade na língua portuguesaThe Portuguese language is one of the most important languages in the world (Luísa Galvão Lessa 2010). Each more people learn Portuguese language. The most important target for learning a language is oral communication with others, and orality as a more important part of language learning, and Portuguese is also a more difficult language to learn than other languages. For oral communication well in the career, That is why learning to speak the Portuguese language has become a topic widely discussed and analyzed in the area of foreign language learning. The development of the Portuguese language in China has little time than other languages, not many people speak it, the orality of the Portuguese language for Chinese students is weak. Therefore, the study of its, factors and measure, has an important guiding meaning in the learning or orality of the Portuguese language for Chinese students. The present study aims to analyze the oral language learning status of the Portuguese language, and difficulties, especially the constraints and effective strategies. based on related theories such as second language acquisition, orality acquisition etc. Using a questionnaire to collect information, the present work aims to analyze and discuss the data and summarize the conclusions with practical meanings. The survey used questionnaires to collect information from 72 participants from different parts of China. In order to analyze and conclude the constraints in the oral learning of the Portuguese language, and according to the learning strategies to deal with oral learning. to provide reference for students to improve and help Chinese students learn Portuguese speaking orality

Towards Multi-modal Interpretation and Explanation

Multimodal task processes on different modalities simultaneously. Visual Question Answering, as a type of multimodal task, aims to answer the natural question answering based on the given image. To understand and process the image, many models to solve the visual question answering task encode the object regions through the convolutional neural network based backbones. Such an image processing method captures the visual features of the object regions in the image. However, the relations between objects are also important information to comprehensively understand the image for answering the complex question, and whether such relational information is captured by the visual features of the object regions remains opaque. To explicitly extract such relational information in images for visual question answering tasks, this research explores an interpretable and structural graph representation to encode the relations between objects. This research works on the three variants of Visual Question Answering tasks with different types of images, including photo-realistic images, daily scene pictures and document pages. Different task-specific relational graphs have been used and proposed to explicitly capture and encode the relations to be used by the proposed models. Such a relational graph provides an interpretable representation of the model inputs and proves its effectiveness in improving the model performance in output prediction. In addition, to improve the interpretation of the model’s prediction, this research also explores the suitable local interpretation method to be applied to the VQA model

Local Interpretations for Explainable Natural Language Processing: A Survey

As the use of deep learning techniques has grown across various fields over the past decade, complaints about the opaqueness of the black-box models have increased, resulting in an increased focus on transparency in deep learning models. This work investigates various methods to improve the interpretability of deep neural networks for natural language processing (NLP) tasks, including machine translation and sentiment analysis. We provide a comprehensive discussion on the definition of the term \textit{interpretability} and its various aspects at the beginning of this work. The methods collected and summarised in this survey are only associated with local interpretation and are divided into three categories: 1) explaining the model's predictions through related input features; 2) explaining through natural language explanation; 3) probing the hidden states of models and word representations.Comment: This work is an initial draf

PDF-VQA: A New Dataset for Real-World VQA on PDF Documents

Document-based Visual Question Answering examines the document understanding of document images in conditions of natural language questions. We proposed a new document-based VQA dataset, PDF-VQA, to comprehensively examine the document understanding from various aspects, including document element recognition, document layout structural understanding as well as contextual understanding and key information extraction. Our PDF-VQA dataset extends the current scale of document understanding that limits on the single document page to the new scale that asks questions over the full document of multiple pages. We also propose a new graph-based VQA model that explicitly integrates the spatial and hierarchically structural relationships between different document elements to boost the document structural understanding. The performances are compared with several baselines over different question types and tasks\footnote{The full dataset will be released after paper acceptance

Deep Structured Feature Networks for Table Detection and Tabular Data Extraction from Scanned Financial Document Images

Automatic table detection in PDF documents has achieved a great success but tabular data extraction are still challenging due to the integrity and noise issues in detected table areas. The accurate data extraction is extremely crucial in finance area. Inspired by this, the aim of this research is proposing an automated table detection and tabular data extraction from financial PDF documents. We proposed a method that consists of three main processes, which are detecting table areas with a Faster R-CNN (Region-based Convolutional Neural Network) model with Feature Pyramid Network (FPN) on each page image, extracting contents and structures by a compounded layout segmentation technique based on optical character recognition (OCR) and formulating regular expression rules for table header separation. The tabular data extraction feature is embedded with rule-based filtering and restructuring functions that are highly scalable. We annotate a new Financial Documents dataset with table regions for the experiment. The excellent table detection performance of the detection model is obtained from our customized dataset. The main contributions of this paper are proposing the Financial Documents dataset with table-area annotations, the superior detection model and the rule-based layout segmentation technique for the tabular data extraction from PDF files

Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis

Recognizing the layout of unstructured digital documents is crucial when parsing the documents into the structured, machine-readable format for downstream applications. Recent studies in Document Layout Analysis usually rely on computer vision models to understand documents while ignoring other information, such as context information or relation of document components, which are vital to capture. Our Doc-GCN presents an effective way to harmonize and integrate heterogeneous aspects for Document Layout Analysis. We first construct graphs to explicitly describe four main aspects, including syntactic, semantic, density, and appearance/visual information. Then, we apply graph convolutional networks for representing each aspect of information and use pooling to integrate them. Finally, we aggregate each aspect and feed them into 2-layer MLPs for document layout component classification. Our Doc-GCN achieves new state-of-the-art results in three widely used DLA datasets.Comment: Accepted by COLING 202

Hypertension and Glycemic Control and Associated Factors for Poor Control in Patient Populations at High Risk of Atherosclerotic Cardiovascular Disease in the Community

BackgroundThe low hypertension control rate or low glycemic control rate in people in the community have been attributed to patients' poor disease awareness and irregular medication in some studies. However, few studies have explored hypertension control rate and/or glycemic control rate in patients with good disease awareness and regular medication.ObjectiveTo investigate the adequate hypertension control rate and/or adequate glycemic control rate in hypertension and diabetic patients who are at high risk of atherosclerotic cardiovascular disease (ASCVD) but have good disease awareness and regular medication, and to explore the reasons for poor control, offering a theoretical basis for better prevention and control of ASCVD.MethodsBy use of cluster sampling, contracted patients with complete data of the China-PAR model who visited 10 community health centers in Shenzhen's Luohu District from August 2018 to April 2019 were selected, and received an assessment for screening the risk of 10-year ASCVD using the China-PAR model, and those with hypertension and/or diabetes who were at high risk of ASCVD (≥10 points) and volunteered to attend this study were further surveyed using a questionnaire developed by our research group. After that, those who were on regular medication with a good understanding of the threats of hypertension and/or diabetes, and targets for blood pressure control and/or fasting glycemia control, were finally enrolled. The rate of adequate hypertension control was compared between those with hypertension, the rate of adequate glycemic control was compared between those with diabetes, and the rates of adequate hypertension and glycemic control were compared between those with both hypertension and diabetes, by demographcihc factors. Then those who were found with inadequate hypertension and/or glycemic control were selected to attend an in-depth, semi-structured individual interview using a descriptive qualitative research design for understating the causes of inadequate hypertension and/or glycemic control. The contents of the interview were coded and categorized using NVivo 12, and were sorted, analyzed, and themes in which were identified using content analysis.ResultsTotally 299 patients were finally enrolled, including 130 (43.5%) with hypertension, 9 (3.0%) with diabetes, and 160 (53.5%) with both hypertension and diabetes. Among the 290 hypertensive patients, 140 (48.3%) had adequate hypertension control. Among the 169 diabetics, 71 (42.0%) had adequate diabetes control. Among the 130 patients with simple hypertension, those with adequate hypertension control had older mean age than did those without (t'=3.758, P<0.001) . Among the 160 patients with both hypertension and diabetes, those with adequate hypertension control had older mean age than did those without (t'=2.203, P=0.031) . Among the 169 patients with diabetes, those with adequate control of fasting glycemia had lower rate of regular exercising (χ2=4.314, P=0.038) and shorter mean duration of diabetes (t=-3.180, P=0.002) , as well as lower mean frequency of blood glucose monitoring (Z=2.228, P=0.026) than did those without. Seven themes emerged from the interview: Patients did not feel compelled to reach the targets, feeling indifferent; Patients gave up after repeated treatments followed by failures to achieve the targets, feeling powerless; Patients took medicines regularly, but had problems in practical medication; Patients were restricted by various realistic factors; Patients were influenced by doctor-related factors, including doctors' irrelevant and ignorant attitudes; Patients had failures due to lack of self-control and unhealthy lifestyles; Other reasons, including unsuccessful medical insurance reimbursement, being afraid of over-control due to previous experiences of too low blood pressure or glucose, etc.ConclusionThe high-risk population of ASCVD who had good disease awareness and took medications regularly still had low hypertension control rate and/or low glycemic control rate. Attention should be specially given to blood pressure levels in young hypertensive patients, and glycemic level in diabetic patients with regular exercising, a long history of diabetes, or frequent blood glucose monitoring. It is necessary to optimize the management of ASCVD in the community by encouraging patients to improve their mindset and change their unhealthy lifestyles, strengthening the promotion of standardized medication use, improving community health services, and improving patients' knowledge, beliefs and behaviors from the biopsychosocial perspective

Context-Dependent T-Box Transcription Factor Family: From Biology to Targeted Therapy

T-BOX factors belong to an evolutionarily conserved family of transcription factors. T-BOX factors not only play key roles in growth and development but are also involved in immunity, cancer initiation, and progression. Moreover, the same T-BOX molecule exhibits different or even opposite effects in various developmental processes and tumor microenvironments. Understanding the multiple roles of context-dependent T-BOX factors in malignancies is vital for uncovering the potential of T-BOX-targeted cancer therapy. We summarize the physiological roles of T-BOX factors in different developmental processes and their pathological roles observed when their expression is dysregulated. We also discuss their regulatory roles in tumor immune microenvironment (TIME) and the newly arising questions that remain unresolved. This review will help in systematically and comprehensively understanding the vital role of the T-BOX transcription factor family in tumor physiology, pathology, and immunity. The intention is to provide valuable information to support the development of T-BOX-targeted therapy