On the Inter-subjectivity in Translation: Viewed From “Distance” in Triangulation Model

Davidson constructs the triangulation model to express the person-person-world interaction in the language communication. This paper discusses the inter-subjectivity among translation subjects based on Davidson’s triangulation model. No translation can be appropriately generated without inter-subjectivity activities. The triangulation model provides a three-dimensional perspective for discussing the interactions among a writer, a source text, a translator, a target text and a target reader. Davidson introduces “distance” and “width” to solve the ambiguity of the cause concept, and this paper focuses on “distance” and creates translation subjects’ triangulation. The paper explores how to achieve the best translation through adjusting the distance to approach the optimized triangle by analyzing the translation practice of Jane Austen’s Emma and aims at guiding the practices using triangulation model

Web-based student response systems and peer instruction: a review and case study

Cooperative learning and peer instruction are well documented pedagogies that engage students in their learning process. The means to implement cooperative learning in the classroom have evolved from raised hands, colored flashcards, student response systems or “clickers”, to web-based audience response systems that work on any electronic device. This paper briefly reviews available audience response systems and presents a case study on Learning Catalytics, a system designed to enable peer instruction and implement just-in-time teaching pedagogy

The Shortcomings and Improvement Strategies of Public Physical Education Courses in Chinese Universities

With the development of China’s higher education, China’s university education has developed rapidly and the number of students enrolled has been increasing. At present, the number of college students in China has exceeded 24 million, showing a prosperous scene. However, with the rapid development of college education, the participation rate of college students in sports is generally low, and the college physical education courses set up to promote college students’ sports have not played their due role. This study analyzes the shortcomings of current college physical education and proposes that college physical education in China should change the current situation of outdated teaching concepts, unreasonable teaching evaluation system, insufficient teaching content, and cultivate college students’ interest in sports and lifelong sports

Game and Intrinsic Motivation: Basketball Teaching for Pupil

The cultivation of pupils’ sports interests is related to the formation of pupils’ sports habits and their physical health in the future. At present, the physical condition of primary and secondary school students in China is not optimistic. Basketball, as a part of school sports, is widely carried out in schools in China. Scientific organization of basketball teaching in primary schools can better improve the physical quality of pupils and cultivate their interest in basketball. Pupils are still in the stage of physical and mental development, and their bones, muscles and nervous system are not well developed, their attention is easy to be distracted, and they can not carry out large amount of exercise, so they should focus on motivational exercise and basic teaching. As a means of teaching, games can improve pupils’ interest in the practice and stimulate their intrinsic motivation to participate in basketball, which should be appropriately increased in teaching

Trends in HIV prevalence and risk behaviours among men who have sex with men from 2013 to 2017 in Nanjing, China: a consecutive cross-sectional survey.

OBJECTIVE:To examine the trends of HIV prevalence, risk behaviours and HIV testing among men who have sex with men (MSM) in Nanjing. DESIGN:Five consecutive cross-sectional surveys. SETTING:Nanjing, China. PRIMARY AND SECONDARY OUTCOME MEASURES:HIV and syphilis prevalence, HIV testing rate and factors associated with HIV infection; demographic characteristics and behaviours. RESULTS:649, 669, 577, 633, 503 MSM were recruited from 2013 to 2017. HIV prevalence was 9.9%, 12.3%, 12.5%, 9.8% and 10.1%, respectively. Syphilis prevalence decreased with a range from 10.6% to 5.6%. Risk behaviours like unprotected anal intercourse (UAI) and unprotected virginal sex in the past 6 months decreased, but multiple sex partners and ever used rush popper rose significantly. MSM tested for HIV in the previous year remained stable from 57.0% to 64.1% (P=0.633). Multivariate analysis showed that tested for HIV in the past year was protective factor against HIV infection. MSM who had UAI in the past 6 months, sex role as receptive and dual, diagnosed with sexually transmitted diseases (STDs) in the past year and currently syphilis infected were risk factors for HIV infection. CONCLUSIONS:We observed stable high HIV prevalence, a steady HIV testing rate, decreasing syphilis prevalence and UAI among MSM in Nanjing. However, rush popper use rose dramatically. The HIV preventive strategies for MSM including condom promotion, HIV testing expansion and reduction of rush popper use, STDs screening and standardised treatment should be strengthened

Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning

Learning transferable knowledge across similar but different settings is a fundamental component of generalized intelligence. In this paper, we approach the transfer learning challenge from a causal theory perspective. Our agent is endowed with two basic yet general theories for transfer learning: (i) a task shares a common abstract structure that is invariant across domains, and (ii) the behavior of specific features of the environment remain constant across domains. We adopt a Bayesian perspective of causal theory induction and use these theories to transfer knowledge between environments. Given these general theories, the goal is to train an agent by interactively exploring the problem space to (i) discover, form, and transfer useful abstract and structural knowledge, and (ii) induce useful knowledge from the instance-level attributes observed in the environment. A hierarchy of Bayesian structures is used to model abstract-level structural causal knowledge, and an instance-level associative learning scheme learns which specific objects can be used to induce state changes through interaction. This model-learning scheme is then integrated with a model-based planner to achieve a task in the OpenLock environment, a virtual ``escape room'' with a complex hierarchy that requires agents to reason about an abstract, generalized causal structure. We compare performances against a set of predominate model-free reinforcement learning(RL) algorithms. RL agents showed poor ability transferring learned knowledge across different trials. Whereas the proposed model revealed similar performance trends as human learners, and more importantly, demonstrated transfer behavior across trials and learning situations.Comment: Accepted to AAAI 2020 as an ora

WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus

In this paper, we introduce a new NLP task -- generating short factual articles with references for queries by mining supporting evidence from the Web. In this task, called WebBrain, the ultimate goal is to generate a fluent, informative, and factually-correct short article (e.g., a Wikipedia article) for a factual query unseen in Wikipedia. To enable experiments on WebBrain, we construct a large-scale dataset WebBrain-Raw by extracting English Wikipedia articles and their crawlable Wikipedia references. WebBrain-Raw is ten times larger than the previous biggest peer dataset, which can greatly benefit the research community. From WebBrain-Raw, we construct two task-specific datasets: WebBrain-R and WebBrain-G, which are used to train in-domain retriever and generator, respectively. Besides, we empirically analyze the performances of the current state-of-the-art NLP techniques on WebBrain and introduce a new framework ReGen, which enhances the generation factualness by improved evidence retrieval and task-specific pre-training for generation. Experiment results show that ReGen outperforms all baselines in both automatic and human evaluations.Comment: Codes in https://github.com/qhjqhj00/WebBrai