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?

MKEAH:Multimodal knowledge extraction and accumulation based on hyperplane embedding for knowledge-based visual question answering

Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding approaches are deficient in representing some complex relations,resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.Methods To this end,we propose MKEAH:Multimodal Knowledge Extraction and Accumulation on Hyperplanes.To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information,two losses are proposed to learn the triplet representations from the complementary views:range loss and orthogonal loss.To interpret the capability of extracting topic-related knowledge,we present the Topic Similarity(TS)between topic and entity-relations.Results Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering.Our model outperformed state-of-the-art methods by 2.12%and 3.24%on two challenging knowledge-request datasets:OK-VQA and KRVQA,respectively.Conclusions The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.

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.

On developing multiple-choice and the washback-Taking cloze as an example

There are several types of cloze. The MC cloze is widely used in national examinations. MC cloze is similar to multiple choice, but not exactly the same. To develop an MC cloze, a suitable passage should be chosen first, then some of the words should be deleted, and finally the distractors for each item are set. To test whether the cloze is validable and reliable, the students are asked to take a pretest. The results are analyzed by GITEST. The data demonstrates that the difficulty level and the discrimination are not good enough. Some of the distractors are too tricky while some others are too weakly distractive.

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.

Structure of the Quarks and a New Model of Protons and Neutrons: Answer to Some Open Questions

The described structural model tries to answer some open questions such as: Why do quarks not exist in the open state? Where are the antiparticles from the Big Bang?

Information Extraction Based on Multi-turn Question Answering for Analyzing Korean Research Trends

Analyzing Research and Development(R&D)trends is important because it can influence future decisions regarding R&D direction.In typical trend analysis,topic or technology taxonomies are employed to compute the popularities of the topics or codes over time.Although it is simple and effective,the taxonomies are difficult to manage because new technologies are introduced rapidly.Therefore,recent studies exploit deep learning to extract pre-defined targets such as problems and solutions.Based on the recent advances in question answering(QA)using deep learning,we adopt a multi-turn QA model to extract problems and solutions from Korean R&D reports.With the previous research,we use the reports directly and analyze the difficulties in handling them using QA style on Information Extraction(IE)for sentence-level benchmark dataset.After investigating the characteristics of Korean R&D,we propose a model to deal with multiple and repeated appearances of targets in the reports.Accordingly,we propose a model that includes an algorithm with two novel modules and a prompt.A newly proposed methodology focuses on reformulating a question without a static template or pre-defined knowledge.We show the effectiveness of the proposed model using a Korean R&D report dataset that we constructed and presented an in-depth analysis of the benefits of the multi-turn QA model.

Deep Multi-Module Based Language Priors Mitigation Model for Visual Question Answering

The original intention of visual question answering(VQA)models is to infer the answer based on the relevant information of the question text in the visual image,but many VQA models often yield answers that are biased by some prior knowledge,especially the language priors.This paper proposes a mitigation model called language priors mitigation-VQA(LPM-VQA)for the language priors problem in VQA model,which divides language priors into positive and negative language priors.Different network branches are used to capture and process the different priors to achieve the purpose of mitigating language priors.A dynamically-changing language prior feedback objective function is designed with the intermediate results of some modules in the VQA model.The weight of the loss value for each answer is dynamically set according to the strength of its language priors to balance its proportion in the total VQA loss to further mitigate the language priors.This model does not depend on the baseline VQA architectures and can be configured like a plug-in to improve the performance of the model over most existing VQA models.The experimental results show that the proposed model is general and effective,achieving state-of-the-art accuracy in the VQA-CP v2 dataset.

Improved Blending Attention Mechanism in Visual Question Answering

Visual question answering(VQA)has attracted more and more attention in computer vision and natural language processing.Scholars are committed to studying how to better integrate image features and text features to achieve better results in VQA tasks.Analysis of all features may cause information redundancy and heavy computational burden.Attention mechanism is a wise way to solve this problem.However,using single attention mechanism may cause incomplete concern of features.This paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention mechanism method.In the case that the attention mechanism will cause the loss of the original features,a small portion of image features were added as compensation.For the attention mechanism of text features,a selfattention mechanism was introduced,and the internal structural features of sentences were strengthened to improve the overall model.The results show that attention mechanism and feature compensation add 6.1%accuracy to multimodal low-rank bilinear pooling network.

The Taiwan Question China From the Perspective of International Crisis Management:History and Future

The Taiwan Question China discussed in this paper belongs to the theoretical crisis discussion on international relations and does not regard the Cross-Strait relations as relations between different countries.The outcome of the 2024 Taiwan Election has a great impact on the Taiwan question,the latest poll shows that the possibility of the Democratic Progressive Party(DPP)candidate to come to power is still very high,because its political evolution trend of Taiwan independence still exists.

旅游自动问答系统中多任务问句分类研究

目前旅游产业信息化建设需要构建旅游自动问答系统,其中问句分类是问答系统的重要组成部分,传统问句类别体系角度单一,且传统分类模型对不平衡的问句数据集表现欠佳。针对这一问题,该文从问题主题和问句答案类型两个角度构建了旅游领域的问句类别体系架构,并提出多任务问句分类模型MT-Bert,在BERT上进行多任务训练,并加入自注意力机制,使用Softmax分类器,并设计了多任务融合损失函数。在山西旅游数据集的结果表明,MT-Bert在两种类别体系的微平均F1值分别为97.6%、91.7%,且避免了非平衡数据的预测失败问题,可以有效处理非平衡数据。

基于知识图谱多跳推理的中文矿物知识问答方法与系统

已有相关矿物数据库用于存储和查询相关矿物知识,常用的搜索引擎也可以对矿物知识进行查询,但无法回答用自然语言进行提问的矿物问题,查询返回的答案需要进一步筛选。亦有基于知识图谱进行矿物知识问答的相关研究,但只能回答涉及知识图谱中一个三元组的简单问题,无法回答涉及多个三元组的多跳复杂问题。为此,本文提出基于知识图谱多跳推理的矿物复杂知识问答方法,采用ComplEx模型将矿物实体、关系和问句表示为复数向量,以更好地获取相互之间的语义及推理关系。输入矿物问句后,通过Bert-LSTM-CRF获取其中心词,采用基于编辑距离及分词的方法获得中心词的候选实体集合,然后采用全连接网络确定最相关的实体作为推理起点,与矿物问句拼接后通过全连接网络获得当前跳的最相关关系。根据当前跳的起始实体及最相关关系,在矿物知识图谱中获得另一实体作为下一跳的推理起点,并将下一跳的问句更新为原问句,与当前跳最相关关系拼接,以将当前跳的推理信息带入到下一跳推理中,直到获得的最相关推理关系为预定义的结束标识符,推理结束,返回最后一跳的实体为答案,并给出推理路径。采用Python语言,在Tensorflow框架下实现了本文提出的矿物复杂知识问答并与相关模型进行对比,证明了本文方法的有效性。采用前后端分离架构,使用RESTful API、React、Ajax、echarts和Flask等框架和技术,开发了基于知识图谱多跳推理的矿物复杂知识问答系统,为矿物知识获取及相关地质研究提供了平台和工具。

基于结构性问题培养全科医生深度思考能力的方法研究

背景住院医师规范化培训的全科学员在各临床专科轮转时间短暂,学习内容宽泛,需要增强学习自主性。全科师资会面临不同背景学员,如5+3、转岗、专业硕士,或3+2助理全科、实习医生、公共卫生学员等,如何在培训中,使不同培训对象能各取所需,达到应有培训效果,需要认真研究。目的探索基于结构性问题的培训方式,提高学员学习主动性,培养其深度思考能力的作用。方法选取2020年江苏省全科/助理全科骨干师资培训班(骨干师资班)和基层卫生人才能力提升培训班(基层人才班)的学员作为研究对象。在每次学习活动结束,即刻组织学员讨论,要求依次回答“通过学习1.你学到了什么?2.还有哪些疑问?3.既往有什么相同或者类似的经验与大家分享?4.对今后工作的启发?”根据上述问题思路自行设计调查问卷调查培训学员对结构性问题培训方式的认同度,分为第1部分一般资料,包括性别、学历、职称、工作单位、岗位、工作年限、参与培训的项目等;第2部分是对“问题1、问题2、问题3、问题4和培训形式5”的解释性问题,5个维度共计20个条目,备选项是四等级,赋值为“1=非常同意,2=同意,3=不太同意,4=完全不同意”。结果培训对象对所有条目选择同意与非常同意的百分比均大于95%。不同性别、年龄、职称、岗位、工作年限培训学员等对结构性问题培训方法的认同程度比较,差异无统计学意义(P>0.05);不同学历、工作单位和培训项目培训学员对结构性问题培训方法的认同程度比较,差异有统计学意义(P<0.05)。相较于研究生及以上学历,本科培训对象对问题3、问题4的认同程度更高(P<0.05)。相较于三级医院,城市社区卫生服务中心(乡镇卫生院)和二级医院的培训对象对于问题1~4认同程度更高(P<0.05)。相较于骨干师资班,基层人才班的培训对象对于问题1~4及培训形式5认同程度更高(P<0.05)。结论培训实践中总结出的四个开放式问题,内容简单,含义递进,具有内在联系。运用结构性问题培训全科学员,形式灵活,能够激发被培训者的深度思考,该方法在基层卫生人才培训班获得更高的认可程度,表明适用于全科医生的培养。

数据科学的科学性与科学问题的分析

作为一门新兴的学科领域,数据科学的科学性受到了关注且其科学问题未明确提出。文中从科学研究范式及方法论、可证伪性和可再现性、科学精神及快速迭代以及科学研究纲领及理论体系4个方面探讨了数据科学的“科学性”,并解答了为什么数据科学是一门新兴科学的问题。在此基础上,结合DIKW模型(DIKW Pyramid or Hierarchy)、DMP(Data-Model-Problem)模型、数据科学的统计学和机器学习方法论以及数据科学的流程与活动,提出了数据科学的7个核心科学问题:解释在先还是在后或无、问题对齐数据还是数据对齐问题、更加相信数据还是模型、更加重视性能还是可解释性、如何划分数据、如何用已知数据解决未知数据的问题、人在环路还是人出环路。最后,提出了数据科学研究的4点建议:聚焦数据科学本身的理论研究,推动数据的科学、技术和工程需要进一步分离和专业化,加强人工智能赋能的数据科学的理论与实践以及数据科学学科(Data Science as A Discipline)与学科中的数据科学(Data Science Within A Discipline)的联动。

基于粗糙分析的大学英语考试质量提升路径

大学英语考试是检验大学英语教学质量和效果的有效手段。将粗糙集理论应用于大学英语考试命题,基于粗糙集的相对正域、冗余属性和属性重要度等概念,给出试题冗余性的定性判别方法,提出试题重要度的定量度量方法,建立基于粗糙分析的大学英语考试质量提升模型。实验结果表明,新型方法发现并修正了部分冗余和重要度较低的试题,有效提高了试卷命题质量,对指导大学英语教学工作、提升教学质量具有重要指导意义。