Factors that impacted mobile-payment adoption in China during the COVID-19 pandemic.

The Unified Theory of Acceptance and Use of Technology (UTAUT) is a potential paradigm for explaining technology adoption and can be applied to a wide range of scenarios. During the COVID-19 (C-19) outbreak in China, mobile-payment platforms (Mpayment) were used extensively in everyday life because they allowed people to avoid direct and indirect connections during transactions, adhere to social-distancing guidelines, and support social-economic stabilization. By exploring the technological and psychological variables that influenced user Mpayment-adoption intentions during the C-19 pandemic, this study broadens the literature on technology adoption in emergency circumstances and expands the UTAUT. A total of 593 complete samples were collected online, with SPSS used for data analysis. The empirical findings reveal that performance expectancy, trust, perceived security, and social influence all had a significant influence on Mpayment acceptance during the C-19 outbreak, with social distancing having the greatest impact, followed by fear of C-19. Interestingly, perceived-effort expectancy had a negative influence on payment acceptance. These findings suggest that future studies should apply the expanded model to different countries and areas to investigate the impact of the C-19 pandemic on Mpayment acceptance.

Enhancing Biomedical ReQA With Adversarial Hard In-Batch Negative Samples.

Question answering (QA) plays a vital role in biomedical natural language processing. Among question answering tasks, the retrieval question answering (ReQA) aims to directly retrieve the correct answer from candidates and has attracted much attention in the community for its efficiency. Recently, researchers have introduced ReQA into the biomedical domain as BioReQA. Typically BioReQA models rely on the dual-encoder to gain semantic representation and are trained following the settings of dense retrieval. However, they normally utilize easy in-batch negative samples in training process to avoid the extra forwarding cost and GPU memory required by encoding additional negative samples. However, hard negative samples have been proved more important with regard to the overall performance of BioReQA tasks. Therefore in this research, we focus on effectively constructing hard in-batch negative samples. Inspired by the classic linear assignment problem, we propose an Iterative Linear Assignment Grouping (ILAG) algorithm to construct hard in-batch negative samples. To further enhance performance for given hard batches in a low-resource scenario, we also employ adversarial training to augment the difficulty of batches. Extensive experiments have shown our proposed method's promising potential in the area of biomedical retrieval question answering.

Adversarial Knowledge Distillation Based Biomedical Factoid Question Answering.

Biomedical factoid question answering is an essential application for biomedical information sharing. Recently, neural network based approaches have shown remarkable performance for this task. However, due to the scarcity of annotated data which requires intensive knowledge of expertise, training a robust model on limited-scale biomedical datasets remains a challenge. Previous works solve this problem by introducing useful knowledge. It is found that the interaction between question and answer (QA-interaction) is also a kind of knowledge which could help extract answer accurately. This research develops a knowledge distillation framework for biomedical factoid question answering, in which a teacher model as the knowledge source of QA-interaction is designed to enhance the student model. In addition, to further alleviate the problem of limited-scale dataset, a novel adversarial knowledge distillation technique is proposed to robustly distill the knowledge from teacher model to student model by constructing perturbed examples as additional training data. By forcing the student model to mimic the predicted distributions of teacher model on both original examples and perturbed examples, the knowledge of QA-interaction can be learned by student model. We evaluate the proposed framework on the widely used BioASQ datasets, and experimental results have shown the proposed method's promising potential.

Improving Biomedical ReQA With Consistent NLI-Transfer and Post-Whitening.

Retrieval Question Answering (ReQA) is an essential mechanism of information sharing which aims to find the answer to a posed question from large-scale candidates. Currently, the most efficient solution is Dual-Encoder which has shown great potential in the general domain, while it still lacks research on biomedical ReQA. Obtaining a robust Dual-Encoder from biomedical datasets is challenging, as scarce annotated data are not enough to sufficiently train the model which results in over-fitting problems. In this work, we first build ReQA BioASQ datasets for retrieving answers to biomedical questions, which can facilitate the corresponding research. On that basis, we propose a framework to solve the over-fitting issue for robust biomedical answer retrieval. Under the proposed framework, we first pre-train Dual-Encoder on natural language inference (NLI) task before the training on biomedical ReQA, where we appropriately change the pre-training objective of NLI to improve the consistency between NLI and biomedical ReQA, which significantly improve the transferability. Moreover, to eliminate the feature redundancies of Dual-Encoder, consistent post-whitening is proposed to conduct decorrelation on the training and trained sentence embeddings. With extensive experiments, the proposed framework achieves promising results and exhibits significant improvement compared with various competitive methods.

Named Entity Aware Transfer Learning for Biomedical Factoid Question Answering.

Biomedical factoid question answering is an important task in biomedical question answering applications. It has attracted much attention because of its reliability. In question answering systems, better representation of words is of great importance, and proper word embedding can significantly improve the performance of the system. With the success of pretrained models in general natural language processing tasks, pretrained models have been widely used in biomedical areas, and many pretrained model-based approaches have been proven effective in biomedical question-answering tasks. In addition to proper word embedding, name entities also provide important information for biomedical question answering. Inspired by the concept of transfer learning, in this study, we developed a mechanism to fine-tune BioBERT with a named entity dataset to improve the question answering performance. Furthermore, we applied BiLSTM to encode the question text to obtain sentence-level information. To better combine the question level and token level information, we use bagging to further improve the overall performance. The proposed framework was evaluated on BioASQ 6b and 7b datasets, and the results have shown that our proposed framework can outperform all baselines.

External features enriched model for biomedical question answering.

BACKGROUND: Biomedical question answering (QA) is a sub-task of natural language processing in a specific domain, which aims to answer a question in the biomedical field based on one or more related passages and can provide people with accurate healthcare-related information. Recently, a lot of approaches based on the neural network and large scale pre-trained language model have largely improved its performance. However, considering the lexical characteristics of biomedical corpus and its small scale dataset, there is still much improvement room for biomedical QA tasks. RESULTS: Inspired by the importance of syntactic and lexical features in the biomedical corpus, we proposed a new framework to extract external features, such as part-of-speech and named-entity recognition, and fused them with the original text representation encoded by pre-trained language model, to enhance the biomedical question answering performance. Our model achieves an overall improvement of all three metrics on BioASQ 6b, 7b, and 8b factoid question answering tasks. CONCLUSIONS: The experiments on BioASQ question answering dataset demonstrated the effectiveness of our external feature-enriched framework. It is proven by the experiments conducted that external lexical and syntactic features can improve Pre-trained Language Model's performance in biomedical domain question answering task.

LSTM-Based End-to-End Framework for Biomedical Event Extraction.

Biomedical event extraction plays an important role in the extraction of biological information from large-scale scientific publications. However, most state-of-the-art systems separate this task into several steps, which leads to cascading errors. In addition, it is complicated to generate features from syntactic and dependency analysis separately. Therefore, in this paper, we propose an end-to-end model based on long short-term memory (LSTM) to optimize biomedical event extraction. Experimental results demonstrate that our approach improves the performance of biomedical event extraction. We achieve average F1-scores of 59.68, 58.23, and 57.39 percent on the BioNLP09, BioNLP11, and BioNLP13's Genia event datasets, respectively. The experimental study has shown our proposed model's potential in biomedical event extraction.

Embedding assisted prediction architecture for event trigger identification.

Molecular events normally have significant meanings since they describe important biological interactions or alternations such as binding of a protein. As a crucial step of biological event extraction, event trigger identification has attracted much attention and many methods have been proposed. Traditionally those methods can be categorised into rule-based approach and machine learning approach and machine learning-based approaches have demonstrated its potential and outperformed rule-based approaches in many situations. However, machine learning-based approaches still face several challenges among which a notable one is how to model semantic and syntactic information of different words and incorporate it into the prediction model. There exist many ways to model semantic and syntactic information, among which word embedding is an effective one. Therefore, in order to address this challenge, in this study, a word embedding assisted neural network prediction model is proposed to conduct event trigger identification. The experimental study on commonly used dataset has shown its potential. It is believed that this study could offer researchers insights into semantic-aware solutions for event trigger identification.