Multi-turn Inference Matching Network for Natural Language Inference

Natural Language Inference (NLI) is a fundamental and challenging task in Natural Language Processing (NLP). Most existing methods only apply one-pass inference process on a mixed matching feature, which is a concatenation of different matching features between a premise and a hypothesis. In this paper, we propose a new model called Multi-turn Inference Matching Network (MIMN) to perform multi-turn inference on different matching features. In each turn, the model focuses on one particular matching feature instead of the mixed matching feature. To enhance the interaction between different matching features, a memory component is employed to store the history inference information. The inference of each turn is performed on the current matching feature and the memory. We conduct experiments on three different NLI datasets. The experimental results show that our model outperforms or achieves the state-of-the-art performance on all the three datasets

The correlation between work motives and organizational behaviors for hospitality employees

Many individuals have worked in the hospitality industry such as serving in a restaurant or housekeeping in a hotel. Individuals have different motives for applying for a hospitality job, and those motives may lead them to perceive the hospitality job differently and behave differently in the hospitality work environment. This study explored employees\u27 work motives based on McClelland\u27s theory of needs, and investigated the effect of work motives on work behaviors (e.g., organizational citizenship behaviors) and attitudes (e.g., job satisfaction and organizational commitment). The study employed a mixed methods approach. First, in-depth individual interviews were conducted with 11 employees from different hospitality segments to explore hospitality employees\u27 work motives. Four themes emerged from analyses of these interviews: job itself, need for achievement, need for affiliation, and need for power. Based on these four themes, a work motive measurement scale was developed. This scale, along with other scales adopted from the literature, was used to develop a paper questionnaire. Data were collected from 388 current and former employees (65.7% response rate) from various hospitality segments (e.g., foodservice and lodging). Data analysis showed that employee work motives have a strong influence on job satisfaction (â= .50, p \u3c .001) and on the presence of organizational citizenship behaviors (â= .43, p \u3c .001). Moreover, job satisfaction strongly and positively influenced organizational commitment (â= .83, p \u3c .001) and organizational commitment positively influence organizational citizenship behavior (â= .28, p \u3c .001). This study provides important evidence of the value of investigating applicants\u27 motives for applying for a job because work motives are strongly related to their work behaviors and attitudes. Interview questions designed to elicit information about individuals\u27 motives for applying for a hospitality job should be included when developing or designing selection and hiring processes. By understanding applicants\u27 work motives, employers can better assess whether applicants are a good fit for the unique characteristics of hospitality jobs and culture, and whether positive job performance can be expected from them

From patterned response dependency to structured covariate dependency: categorical-pattern-matching

Data generated from a system of interest typically consists of measurements from an ensemble of subjects across multiple response and covariate features, and is naturally represented by one response-matrix against one covariate-matrix. Likely each of these two matrices simultaneously embraces heterogeneous data types: continuous, discrete and categorical. Here a matrix is used as a practical platform to ideally keep hidden dependency among/between subjects and features intact on its lattice. Response and covariate dependency is individually computed and expressed through mutliscale blocks via a newly developed computing paradigm named Data Mechanics. We propose a categorical pattern matching approach to establish causal linkages in a form of information flows from patterned response dependency to structured covariate dependency. The strength of an information flow is evaluated by applying the combinatorial information theory. This unified platform for system knowledge discovery is illustrated through five data sets. In each illustrative case, an information flow is demonstrated as an organization of discovered knowledge loci via emergent visible and readable heterogeneity. This unified approach fundamentally resolves many long standing issues, including statistical modeling, multiple response, renormalization and feature selections, in data analysis, but without involving man-made structures and distribution assumptions. The results reported here enhance the idea that linking patterns of response dependency to structures of covariate dependency is the true philosophical foundation underlying data-driven computing and learning in sciences.Comment: 32 pages, 10 figures, 3 box picture

EFFECT OF GENDER ON THE CONSUMER’S CHANNEL SELECTION OF FAST FASHION APPAREL PRODUCTS

This paper takes the consumers of fast fashion clothing products as research subjects, and EMB model as logistic basis to compare and analyze the preference of male and female on three channels including Internet, mail order catalogue (telephone ordering) and entity shops in the whole purchasing process (including need recognition, information search, alternative evaluation, purchase, and after purchase evaluation) with the influential factors by means of questionnaire. Throughout the research, it is concluded that the consumers of fast fashion clothing products of different gender consumers have difference in preference on the channels in different stages of purchasing decision. It has great reference significance that Fast Fashion Costume makes the effective market strategies according to gender difference