Look at the First Sentence: Position Bias in Question Answering

Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity and effectiveness. In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions. We first illustrate this position bias in popular extractive QA models such as BiDAF and BERT and thoroughly examine how position bias propagates through each layer of BERT. To safely deliver position information without position bias, we train models with various de-biasing methods including entropy regularization and bias ensembling. Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias, recovering the performance of BERT from 37.48% to 81.64% when trained on a biased SQuAD dataset.Comment: 13 pages, EMNLP 202

SAIN: Self-Attentive Integration Network for Recommendation

With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where user-item interactions are sparse. To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed. Since these models use auxiliary attributes, they are effective in cold start settings. However, most of the proposed models are either unable to capture complex feature interactions or not properly designed to combine user-item feedback information with content information. In this paper, we propose Self-Attentive Integration Network (SAIN) which is a model that effectively combines user-item feedback information and auxiliary information for recommendation task. In SAIN, a self-attention mechanism is used in the feature-level interaction layer to effectively consider interactions between multiple features, while the information integration layer adaptively combines content and feedback information. The experimental results on two public datasets show that our model outperforms the state-of-the-art models by 2.13%Comment: SIGIR 201

Meeting Planners’ Perspectives on Relationship Selling in the MICE Industry

The purpose of this study is to propose a newly refined model of relationship selling in the general context between meeting planners and suppliers in Meeting, Incentive, Convention, and Exhibition (MICE) industry .The refined model was tested by using an online survey of a sample of professional meeting planners in the Meeting Professionals International (MPI) and Professional Convention Management Association (PCMA). Results showed both social bonds with the supplier and expertise of the supplier had an effect on meeting planners’ perceived trust and satisfaction; while willingness and power of the supplier was related to neither meeting planners’ perceived trust nor satisfaction. This refined model could provide research insights and guide future research on key relationship selling constructs between meeting planners and suppliers in the MICE industry

How can airlines’ ESG activities be evaluated by consumers? Development of consumer-driven ESG measurement to validation

Despite the inclusion of environmental, social, and governance (ESG) initiatives seeming ubiquitous in today’s corporate practices, there is still a lack of systematic research addressed on how the corporate\u27s ESG activities affect consumers\u27 overall perception. This study aims to determine whether perceived airline ESG practices affect consumers’ behavioral intention and if brand image, attachment, and attitude mediate this relationship. Initially the ESG measurement of consumer perception was developed for the airline industry, and then an online survey was conducted of 567 South Korean consumers. Data was analyzed by using the partial least squares structural equation modeling. Our research findings provide insights from consumers\u27 perspectives by analyzing multi-dimensional E, S, and G initiatives perceptions that differentiate influences on brand image, attachment, attitude, and consumers’ behavioral intentions. This study emphasizes the importance of “governance” among ESG construct in increasing airline brand image, attachment, and consumer attitude that ultimately impact consumers’ behavioral intention

Factors Affecting the Self-directed Learning of Students at Clinical Practice Course for Advanced Practice Nurse

PurposeThe current study aimed to examine the casual relationships among belongingness during clinical practice, stress, satisfaction with clinical practice, and self-esteem, which are factors affecting the self-directed learning that results from the clinical practice of advanced practice nurse (APN) students.MethodsData were collected between April 5 and May 19, 2010, from 202 students in 11 APN training institutions located in and outside of Seoul, who were selected using convenience sampling. For hypothesis testing, the collected data were analyzed using AMOS 8.0.ResultsAnalysis of the path coefficients in this study showed that 37% of the variation in self-directed learning could be explained by variations in the model. Self-esteem and belongingness during clinical practice directly affected the self-directed learning of APN students, and belongingness also had an indirect effect via self-esteem. However, stress and satisfaction with clinical practice had no significant mediating effect on self-directed learning. At the same time, belongingness during clinical practice was found to be a good predictive factor to explain stress and satisfaction with clinical practice.ConclusionsThis study demonstrated the hierarchical relationship among belongingness, self-esteem, and self-directed learning based on the conceptual framework developed by Levett-Jones and Lathlean, thus proving the usefulness of this framework for application in the field. Therefore, this study found that there are needs of high self-esteem and belongingness in order to improve self-directed learning for APN students in clinical practice

A Review on the Computational Methods for Emotional State Estimation from the Human EEG

A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions.open