俄语学习策略、动机和效果的相关性探究—基于济宁学院的实证研究

语言学习策略和学习动机是促进第二外语学习的必要手段.俄语是一门复杂多变的屈折语,想要取得理想的学习效果,各种学习策略的作用不可小觑.济宁学院《二外俄语学习者学习策略调查表》中的数据显示:俄语学习者的学习策略、动机和效果之间有一定相关性.探究这种相关性,对提高学生的的学习效果大有裨益.     

用图式理论改革大学英语听力教学的探讨

学生在语言功底与背景知识方面的差异性是我国各层次本科院校普遍面临的问题。根据认知语言学领域的图式理论,教师可以通过分级教学、竞赛教学以及组织第二课堂活动的方式,有针对性地填补学生在语言图式和内容图式方面存在的空白。     

我国民族传统体育文化的价值结构研究

摘 要：运用文献资料法、逻辑思维等方法,以我国民族传统体育文化的价值结构为研究对象,研究表明：人或人群根据我国民族传统体育所具有的依赖性、持久性、普遍性的特点,把我国民族传统体育价值文化分为实用价值、物化价值、精神价值。实用价值是实实在在的、能看得到、计算到的、有价可寻的,随着时间的变迁其价值也会发生改变,对民族传统体育依赖性较强,在价值文化中处在表层位置。物化价值的“实物”已失去原有意义,而是赋予另一种意义,这种价值一旦形成保持的时间较长,在价值文化处在中间层次。精神价值主要体现了我国各民族的共同民族精神,具有持久性和稳定性,只有在民族被同化或消亡时其精神价值才会根本改变,对民族传统体育的“物”依赖性最弱,但在民族传统体育文化的价值文化中所处在核心位置。研究目的是为保护和发展民族传统体育提供好的方法。     

认知心理视角下的壮母语学生英语学习双迁移研究

壮母语学生英语学习有其自身特点,英语的学习过程是其母语、汉语和英语交互影响相互作用的过程,这三语间相互影响、作用或干扰导致其语言迁移的动态性、复杂和多样性。从心理认知的角度来研究壮母语学生英语学习中的语言迁移现象,可帮助我们进一步认清壮族学生语言习得中的障碍,把握好教学内容的难重点,帮助壮族学生克服语言学习中的困难。     

发育协调障碍儿童运动干预研究进展

发育协调障碍(DCD)是一种常见的神经发育问题,常见于儿童。DCD儿童具有显著的认知缺陷和不及同龄儿童的身体机能水平。当前对于DCD儿童的运动干预并没有明确的方案。通过介绍DCD研究的发展过程,分析DCD儿童的认知特征和身体机能水平,展示当前常见的运动干预手段,并为运动干预研究提供建议,有助于今后相关研究的开展。     

Reviewer assignment algorithms for peer review automation: A survey

Assigning paper to suitable reviewers is of great significance to ensure the accuracy and fairness of peer review results. In the past three decades, many researchers have made a wealth of achievements on the reviewer assignment problem (RAP). In this survey, we provide a comprehensive review of the primary research achievements on reviewer assignment algorithm from 1992 to 2022. Specially, this survey first discusses the background and necessity of automatic reviewer assignment, and then systematically summarize the existing research work from three aspects, i.e., construction of candidate reviewer database, computation of matching degree between reviewers and papers, and reviewer assignment optimization algorithm, with objective comments on the advantages and disadvantages of the current algorithms. Afterwards, the evaluation metrics and datasets of reviewer assignment algorithm are summarized. To conclude, we prospect the potential research directions of RAP. Since there are few comprehensive survey papers on reviewer assignment algorithm in the past ten years, this survey can serve as a valuable reference for the related researchers and peer review organizers.     

Fake news detection via knowledgeable prompt learning

The spread of fake news has become a significant social problem, drawing great concern for fake news detection (FND). Pretrained language models (PLMs), such as BERT and RoBERTa can benefit this task much, leading to state-of-the-art performance. The common paradigm of utilizing these PLMs is fine-tuning, in which a linear classification layer is built upon the well-initialized PLM network, resulting in an FND mode, and then the full model is tuned on a training corpus. Although great successes have been achieved, this paradigm still involves a significant gap between the language model pretraining and target task fine-tuning processes. Fortunately, prompt learning, a new alternative to PLM exploration, can handle the issue naturally, showing the potential for further performance improvements. To this end, we propose knowledgeable prompt learning (KPL) for this task. First, we apply prompt learning to FND, through designing one sophisticated prompt template and the corresponding verbal words carefully for the task. Second, we incorporate external knowledge into the prompt representation, making the representation more expressive to predict the verbal words. Experimental results on two benchmark datasets demonstrate that prompt learning is better than the baseline fine-tuning PLM utilization for FND and can outperform all previous representative methods. Our final knowledgeable model (i.e, KPL) can provide further improvements. In particular, it achieves an average increase of 3.28% in F1 score under low-resource conditions compared with fine-tuning.     

科技人才称号的本质、异化与回归

科技人才称号的管理与使用,事关科技强国建设战略大计。近年来,科技人才称号使用中出现的问题广为诟病,引发全社会对诸多科技人才称号管理问题的深沉担忧。因此,系统研究科技人才称号的历史渊源、内涵本质、异化问题,进而找到科技人才称号的本质回归之道,具有重要的理论和实践价值。从历史看,科技人才称号因科技人才计划实施而生,有其历史必然性、存在合理性。从内涵本质看,科技人才称号是特定科技人才计划的入选身份标识、促进科技人才成长的激励机制、赋予杰出科技人才的学术荣誉,更是科技人才的社会责任和使命担当。但现实中因科技人才称号使用标签化、终身化、商品化、权力化问题异化了科技人才称号的本质,需进一步优化人才计划顶层设计、实施人才项目精细化管理、改革人才评价制度体系、强化人才责任使命担当、重塑社会人才价值观念,以回归科技人才称号的本质,发挥科技人才的示范带动作用。     

A Dual-Pointer guided transition system for end-to-end structured sentiment analysis with global graph reasoning

Structured sentiment analysis is a newly proposed task, which aims to summarize the overall sentiment and opinion status on given texts, i.e., the opinion expression, the sentiment polarity of the opinion, the holder of the opinion, and the target the opinion towards. In this work, we investigate a transition-based model for end-to-end structured sentiment analysis task. We design a transition architecture which supports the recognition of all the possible opinion quadruples in one shot. Based on the transition backbone, we then propose a Dual-Pointer module for more accurate term boundary detection. Besides, we further introduce a global graph reasoning mechanism, which helps to learn the global-level interactions between the overlapped quadruples. The high-order features are navigated into the transition system to enhance the final predictions. Extensive experimental results on five benchmarks demonstrate both the prominent efficacy and efficiency of our system. Our model outperforms all baselines in terms of all metrics, especially achieving a 10.5% point gain over the current best-performing system only detecting the holder-target-opinion triplets. Further analyses reveal that our framework is also effective in solving the overlapping structure and long-range dependency issues.     

Transfer language selection for zero-shot cross-lingual abusive language detection

We study the selection of transfer languages for automatic abusive language detection. Instead of preparing a dataset for every language, we demonstrate the effectiveness of cross-lingual transfer learning for zero-shot abusive language detection. This way we can use existing data from higher-resource languages to build better detection systems for low-resource languages. Our datasets are from seven different languages from three language families. We measure the distance between the languages using several language similarity measures, especially by quantifying the World Atlas of Language Structures. We show that there is a correlation between linguistic similarity and classifier performance. This discovery allows us to choose an optimal transfer language for zero shot abusive language detection.