Special Issue:Questions&Data for Better Science and Innovation Call for submissions

Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?

PAL-BERT:An Improved Question Answering Model

In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant attention.However,as algorithms,data,and computing power advance,the issue of increasingly larger models and a growing number of parameters has surfaced.Consequently,model training has become more costly and less efficient.To enhance the efficiency and accuracy of the training process while reducing themodel volume,this paper proposes a first-order pruningmodel PAL-BERT based on the ALBERT model according to the characteristics of question-answering(QA)system and language model.Firstly,a first-order network pruning method based on the ALBERT model is designed,and the PAL-BERT model is formed.Then,the parameter optimization strategy of the PAL-BERT model is formulated,and the Mish function was used as an activation function instead of ReLU to improve the performance.Finally,after comparison experiments with traditional deep learning models TextCNN and BiLSTM,it is confirmed that PALBERT is a pruning model compression method that can significantly reduce training time and optimize training efficiency.Compared with traditional models,PAL-BERT significantly improves the NLP task’s performance.

Operational requirements analysis method based on question answering of WEKG

The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering(QA) of weapons and equipment knowledge graph(WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.

DPAL-BERT:A Faster and Lighter Question Answering Model

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

Questioning Styles in Vocational College English Classrooms

This study investigates the effects of TBLT reform in Higher Vocational Colleges from the perspective of questioning styles.It employs three methods to collect data:classroom observation,semi-structured interviews and focus group discussion with eight English teachers and their 384 non-English major students from three Higher Vocational Colleges in Guangdong.The results indicated that the teachers assigned students different tasks to perform in class.They seemed to be adopting the TBLT approach,but their English classes were not totally different from the teacher-centered grammar-focused lessons,the student-centered or communicative lessons.

A Study on Teacher's Questioning Strategies in the UK

Classroom questioning is one of the main means for classroom interaction which plays a very important role in classroom teaching. Therefore, based on the observation of four different level English classes in the UK and interview of English teachers, this thesis investigates the types, functions and answer-seeking strategies used by EFL teachers.

An Investigation of Teacher Questioning Strategies Usage in College English Course——A Case Study on Honghe University

Teacher questioning strategy is one of popular teaching method in teaching practice.The teachers and students of college English course II was taken as investigate objects in this study.This study will focus on the problems of questioning frequency,questioning types,questioning strategies,waiting time and students’response to deeply study this situation.Through the data collection and analysis,the researcher hopes the study will deeply reveal the real situation of teacher questioning usage in Honghe University.Some referential suggestions will be offered to the college English teachers for their future teaching.

Application of Questioning Strategy in English Reading Teaching

This paper mainly deals with the application of questioning strategy in English reading teaching.As one of the most useful methods,teachers usually attach great importance to questioning,which is proved to be very effective in teaching.

The Role of Questioning in the Teaching of Reading

In learner-centered classroom,teachers' questioning serves as very important stimuli to foster students' participation in classroom interaction.A classroom full of active interactions is an ideal environment for foreign language acquisition(FLA).Questioning is a critical strategy for the instructors to engage students in reading activities so as to help them build the habit of independent learning.

Teachers’Questioning Strategies in Reading Class—the Perspective of Adaptation

Teachers’questioning is one of the crucial means for classroom interaction.This study attempts to explore teachers’questioning strategies in reading class from the perspective of Adaptation Theory.It is found that the effective questioning can be realized in teachers’adaptation to their roles,the features of students and the features of reading courses.

A Self-reflection on the Questioning in the English Class in Vocational College

Questioning is a indispensible part of classroom teaching and a measurement of classroom performance in our vocational college. But there is not enough importance had attached on it in the author's class. In this essay,the author researched on previous theories and peer studies on questioning,in accordance on the specific situation of our college,trying to figure out how to improve the questioning behavior in the class.

Brief Introduction about Questioning Art in English Classroom

here is no doubt that teacher’s language has deep effects on the EFL classroom language teaching.To make a further explanation about how and why important teacher’s language is,this paper intends to make more clear elucidation about its strategies according to the following specific parts.

Questioning Practice in Oral Teaching for Higher Vocational Students

In order to improve the students’ language competence,the author paid much attention to the students’ questioning practice.It,to some extent,turns out to be an effective method to train the students to use appropriate language grammatically and sociallinguistically.

Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic interest.Scholars have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs engender.To clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature review.The goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)users.The final review included 133 articles.Dominant research themes include question quality,answer quality,and expert identification.In terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack Overflow.The scope of most articles was confined to just one platform with few cross-platform investigations.Articles with ML outnumber those with DL.Nonetheless,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering

Recently,pre-trained language representation models such as bidirec-tional encoder representations from transformers(BERT)have been performing well in commonsense question answering(CSQA).However,there is a problem that the models do not directly use explicit information of knowledge sources existing outside.To augment this,additional methods such as knowledge-aware graph network(KagNet)and multi-hop graph relation network(MHGRN)have been proposed.In this study,we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers(ALBERT)with knowledge graph information extraction technique.We also propose to applying the novel method,schema graph expansion to recent language models.Then,we analyze the effect of applying knowledge graph-based knowledge extraction techniques to recent pre-trained language models and confirm that schema graph expansion is effective in some extent.Furthermore,we show that our proposed model can achieve better performance than existing KagNet and MHGRN models in CommonsenseQA dataset.

Expert Recommendation in Community Question Answering via Heterogeneous Content Network Embedding

ExpertRecommendation(ER)aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering(CQA)web services.How to model questions and users in the heterogeneous content network is critical to this task.Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues.Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for modeling.However,they often fail to distinguish the nodes’personalized preferences and only consider the textual content of a part of the nodes in network embedding learning,while ignoring the semantic relevance of nodes.In this paper,we propose a novel framework that jointly considers the structural proximity relations and textual semantic relevance to model users and questions more comprehensively.Specifically,we learn topology-based embeddings through a hierarchical attentive network learning strategy,in which the proximity information and the personalized preference of nodes are encoded and preserved.Meanwhile,we utilize the node’s textual content and the text correlation between adjacent nodes to build the content-based embedding through a meta-context-aware skip-gram model.In addition,the user’s relative answer quality is incorporated to promote the ranking performance.Experimental results show that our proposed framework consistently and significantly outperforms the state-of-the-art baselines on three real-world datasets by taking the deep semantic understanding and structural feature learning together.The performance of the proposed work is analyzed in terms of MRR,P@K,and MAP and is proven to be more advanced than the existing methodologies.

Questioning Strategies of English Teachers

AMY.B.M.TSUI thinks that most of the interactive learning starts with the teachers' questioning. Richard and Lockart(1994) think that the proper questioning can help the students to acquire the second language. Jin Chuanbao(1997) even thinks that the questioning process of the teachers should become the core of the class. Though almost all of the teachers are questioning, they know little about the questioning strategies. In this case, it is urgent to study this subject. The present study reveals some problems of four teachers' questioning strategies in Junior Middle School. I hope some helpful ideas can be found in the thesis.

A Study on Teacher’s Questioning Practice in an ESL Learning Context in Hong Kong

This empirical study intends to explore the questioning behaviors of an English as a second language(hereinafter referred to as ESL)teacher in Hong Kong by quantitatively looking at the distribution of the two types of questions,namely display questions and referential questions,as well as by qualitatively evaluating the universally accepted functions of the questions and the effectiveness of the modification techniques used to enhance the factual value of the questions.Data-based explorations challenging the traditional views toward questions are critically presented,and new findings are excavated and advocated.Pedagogical implications are considerably raised as they serve as a theoretical framework to be applied and further analyzed in future real-life EFL and ESL settings,so as to realize better assessment for learning.