Link Prediction for Wikipedia Articles as a Natural Language Inference Task

Link prediction task is vital to automatically understanding the structure of large knowledge bases. In this paper, we present our system to solve this task at the Data Science and Advanced Analytics 2023 Competition "Efficient and Effective Link Prediction" (DSAA-2023 Competition) with a corpus containing 948,233 training and 238,265 for public testing. This paper introduces an approach to link prediction in Wikipedia articles by formulating it as a natural language inference (NLI) task. Drawing inspiration from recent advancements in natural language processing and understanding, we cast link prediction as an NLI task, wherein the presence of a link between two articles is treated as a premise, and the task is to determine whether this premise holds based on the information presented in the articles. We implemented our system based on the Sentence Pair Classification for Link Prediction for the Wikipedia Articles task. Our system achieved 0.99996 Macro F1-score and 1.00000 Macro F1-score for the public and private test sets, respectively. Our team UIT-NLP ranked 3rd in performance on the private test set, equal to the scores of the first and second places. Our code is publicly for research purposes.Comment: Accepted at the 10th IEEE International Conference On Data Science And Advanced Analytics (DSAA 2023

Barriers Affecting the Acceptance of Tourism Apps Among Tourists

The development of technology applications has brought many benefits to users. However, users face certain risks when adopting new technology applications. This leads to barriers affecting the acceptance of new technology applications. The study was carried out to identify the barriers affecting the acceptance of tourism apps by tourists. Research data were collected by the method of quota sampling, with a sample size of 222 tourists who have visited and experienced tourism services at famous destinations in Vietnam. Qualitative research methods and quantitative research methods are both applied to test the research hypotheses. The structural equation modeling (SEM) has demonstrated that insecurity and discomfort positively affect tourists’ perceived risk of tourism apps. Also, insecurity, discomfort, and perceived risk negatively affect tourists’ acceptance of travel apps

ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing

English and Chinese, known as resource-rich languages, have witnessed the strong development of transformer-based language models for natural language processing tasks. Although Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT, ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and named entity recognition. These pre-trained language models are still limited to Vietnamese social media tasks. In this paper, we present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our pre-trained model on five important natural language downstream tasks on Vietnamese social media texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters, surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our ViSoBERT model is available only for research purposes.Comment: Accepted at EMNLP'2023 Main Conferenc

ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in Vietnamese

Social media processing is a fundamental task in natural language processing with numerous applications. As Vietnamese social media and information science have grown rapidly, the necessity of information-based mining on Vietnamese social media has become crucial. However, state-of-the-art research faces several significant drawbacks, including imbalanced data and noisy data on social media platforms. Imbalanced and noisy are two essential issues that need to be addressed in Vietnamese social media texts. Graph Convolutional Networks can address the problems of imbalanced and noisy data in text classification on social media by taking advantage of the graph structure of the data. This study presents a novel approach based on contextualized language model (PhoBERT) and graph-based method (Graph Convolutional Networks). In particular, the proposed approach, ViCGCN, jointly trained the power of Contextualized embeddings with the ability of Graph Convolutional Networks, GCN, to capture more syntactic and semantic dependencies to address those drawbacks. Extensive experiments on various Vietnamese benchmark datasets were conducted to verify our approach. The observation shows that applying GCN to BERTology models as the final layer significantly improves performance. Moreover, the experiments demonstrate that ViCGCN outperforms 13 powerful baseline models, including BERTology models, fusion BERTology and GCN models, other baselines, and SOTA on three benchmark social media datasets. Our proposed ViCGCN approach demonstrates a significant improvement of up to 6.21%, 4.61%, and 2.63% over the best Contextualized Language Models, including multilingual and monolingual, on three benchmark datasets, UIT-VSMEC, UIT-ViCTSD, and UIT-VSFC, respectively. Additionally, our integrated model ViCGCN achieves the best performance compared to other BERTology integrated with GCN models

Biomolecular evaluation of three contrasting rice cultivars (Oryza sativa L.) in salt stress response at seedling stage.

Salt contamination of soils due to climate change faces a severe environmental issue that affects crop production today. However, the response mechanism in plants to salt stress is not fully understood. The present study investigated molecular and biochemical changes under salt stress in rice seedlings of three rice cultivars, i.e., AGPPS114 (salt-tolerant), OM6967 (moderately tolerance), VD20 (salt-sensitive). Increasing salt concentration leads to a reduction in shoot/root length but different levels among the cultivars. In contrast, reactive oxygen species (ROS) accumulation and lipid peroxidation increased progressively with increasing salt concentration and time course treatment. However, at 250 ?M of NaCl, these parameters were more adversely affected in VD20 than AGPPS114 and OM6967. Using ICP showed that Na+ accumulation in rice root increased gradually with increasing NaCl concentrations in all cultivars under salt treatment but was low in salt-sensitive cultivar VD20 compared to other cultivars. Antioxidant enzyme activity analysis indicated catalase (CAT) and superoxide dismutase (SOD) were induced during salt treatment in all cultivars. The results also showed greater proline and glycine betaine accumulation in the AGPPS114 than OM6976 and VD20. qPCR indicated a significant difference in transcript levels of the Na+-transporter gene OsSOS1, OsNHX1 and OsHKT1s in AGPPS114 and OM6967 cultivars compared to VD20 cultivar. In summary, the active regulation of genes related to Na+ transport at the transcription level and with high glycine betaine and proline accumulation levels may be involved in salt tolerance mechanisms and thus might be useful for selecting tolerant plants

THE RELATIONSHIP BETWEEN COOPERATION, SUPPLY CHAIN PERFORMANCE AND TOUR OPERATOR PERFORMANCE: A CASE STUDY OF TOURISM SUPPLY CHAIN IN VIETNAM

Cooperation among members in the supply chain is essential and is a core element in the supply chain management strategy. This article uses a combination of qualitative and quantitative analysis methods. Structural equation modeling (SEM) demonstrates the relationship between cooperation, tourism supply chain performance, and operator performance in Vietnam. Research collected data from 242 domestic and international tour operators located in major Vietnam cities such as Ho Chi Minh City, Hanoi City, Da Nang City, and Can Tho City. The study has demonstrated that cooperation positively affects tourism supply chain performance and operator performance. Besides, tourism supply chain performance benefits operator performance in the Vietnamese tourism supply chain