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.

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.

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.

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.

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.

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.

Designing an automated FAQ answering system for farmers based on hybrid strategies

Purpose: The purpose of this study is to develop an automated frequently asked question(FAQ) answering system for farmers. This paper presents an approach for calculating the similarity between Chinese sentences based on hybrid strategies.Design/methodology/approach: We analyzed the factors influencing the successful matching between a user's question and a question-answer(QA) pair in the FAQ database. Our approach is based on a combination of multiple factors. Experiments were conducted to test the performance of our method.Findings: Experiments show that this proposed method has higher accuracy. Compared with similarity calculation based on TF-IDF,the sentence surface forms and the semantic relations,the proposed method based on hybrid strategies has a superior performance in precision,recall and F-measure value.Research limitations: The FAQ answering system is only capable of meeting users' demand for text retrieval at present. In the future,the system needs to be improved to meet users' demand for retrieving images and videos.Practical implications: This FAQ answering system will help farmers utilize agricultural information resources more efficiently.Originality/value: We design the algorithms for calculating similarity of Chinese sentences based on hybrid strategies,which integrate the question surface similarity,the question semantic similarity and the question-answer similarity based on latent semantic analysis(LSA) to find answers to a user's question.

Automatic Question Answering from Web Documents

A passage retrieval strategy for web-based question answering （QA） systems is proposed in our QA system. It firstly analyzes the question based on semantic patterns to obtain its syntactic and semantic information and then form initial queries. The queries are used to retrieve documents from the World Wide Web （WWW） using the Google search engine. The queries are then rewritten to form queries for passage retrieval in order to improve the precision. The relations between keywords in the question are employed in our query rewrite method. The experimental result on the question set of the TREC-2003 passage task shows that our system performs well for factoid questions.

Query Expansion Based on Semantics and Statistics in Chinese Question Answering System

In Chinese question answering system, because there is more semantic relation in questions than that in query words, the precision can be improved by expanding query while using natural language questions to retrieve documents. This paper proposes a new approach to query expansion based on semantics and statistics Firstly automatic relevance feedback method is used to generate a candidate expansion word set. Then the expanded query words are selected from the set based on the semantic similarity and seman- tic relevancy between the candidate words and the original words. Experiments show the new approach is effective for Web retrieval and out-performs the conventional expansion approaches.

A Novel Bidirectional LSTM and Attention Mechanism Based Neural Network for Answer Selection in Community Question Answering

Deep learning models have been shown to have great advantages in answer selection tasks.The existing models,which employ encoder-decoder recurrent neural network(RNN),have been demonstrated to be effective.However,the traditional RNN-based models still suffer from limitations such as 1)high-dimensional data representation in natural language processing and 2)biased attentive weights for subsequent words in traditional time series models.In this study,a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory(Bi-LSTM)and attention mechanism.The proposed model is able to generate the more effective question-answer pair representation.Experiments on a question answering dataset that includes information from multiple fields show the great advantages of our proposed model.Specifically,we achieve a maximum improvement of 3.8%over the classical LSTM model in terms of mean average precision.

Probabilistic Quantitative Temporal Constraints:Representing,Reasoning,and Query Answering

In many applications and domains,temporal constraints between actions, and their probabilities play an important role. We propose the first approach in the literature coping with probabilistic quantitative constraints. To achieve such a challenging goal, we extend the widely used simple temporal problem(STP) framework to consider probabilities.Specifically,we propose i) a formal representation of probabilistic quantitative constraints, ii) an algorithm,based on the operations of intersection and composition,for the propagation of such temporal constraints, and iii) facilities to support query answering on a set of such constraints. As a result, we provide users with the first homogeneous method supporting the treatment(representing,reasoning,and querying) of probabilistic quantitative constraints, as required by many applications and domains.

Application of Question and Answering on Virtual Human Dialogue:a Review and Prediction

Nowadays,virtual human(VH) is becoming a hot research topic in virtualization.VH dialogue can be categorized as an application of natural language processing(NLP) technology,since it is relational to question and answering(QA) technologies.In order to integrate these technologies,this paper reviews some important work on VH dialogue,and predicts some research points on the view of QA technologies.

IVM Telephone Answering:为家庭增添一部免费的电话语音答录机

当电脑网络进入家庭,人与社会拉得更近,世界也随之变小。每一个拥有电脑的家庭,总希望它能够集成更多的功能,为我们带来更多的方便,甚至当我们有事不在时,它也能够帮助我们“自动地”应付一些简单的工作。IVMTelephone Answering就是这样一款小巧而可爱的软件,它使你不会再为错过重要的电话而烦恼,从而提高工作效率。有了它,你不必再花费昂贵的费用去购置一台录音电话,只要你的电脑拥有支持语音的Modem,就可以完成全部电话答录,甚至更多的功能。

Answer Classification via Machine Learning in Community Question Answering

As a new type of knowledge sharing platform,the community question answer website realizes the acquisition and sharing of knowledge,and is loved and sought after by the majority of users.But for multi-answer questions,answer quality assessment becomes a challenge.The answer selection in CQA(Community Question Answer)was proposed as a challenge task in the SemEval competition,which gave a data set and proposed two subtasks.Task-A is to give a question(including short title and extended description)and its answers,and divide each answer into absolutely relevant(good),potentially relevant(potential)and bad or irrelevant(bad,dialog,non-English,other).Task-B is to give a YES/NO type question(including short title and extended description)and some answers.Based on the answer of the absolute correlation type(good),judge whether the answer to the whole question should be yes,no or uncertain.This paper first preprocesses this data set,and then uses natural language processing technology to perform word segmentation,part-of-speech tagging and named entity recognition on the data set,and then perform feature extraction on the preprocessed data set.Finally,SVM and random forest are used to classify on the basis of feature extraction,and the classification results are analyzed and compared.The experiments in this paper show that SVM and random forest methods have good results on the data set,and exceed the multi-classifier ensemble learning method and hierarchical classification method proposed by the predecessors.

Superimposed Attention Mechanism-Based CNN Network for Reading Comprehension and Question Answering

In recent years, end-to-end models have been widely used in the fields of machine comprehension (MC) and question answering (QA). Recurrent neural network (RNN) or convolutional neural network (CNN) is combined with attention mechanism to construct models to improve their accuracy. However, a single attention mechanism does not fully express the meaning of the text. In this paper, recurrent neural network is replaced with the convolutional neural network to process the text, and a superimposed attention mechanism is proposed. The model was constructed by combining a convolutional neural network with a superimposed attention mechanism. It shows that good results are achieved on the Stanford question answering dataset (SQuAD).

A comparative analysis of major Chinese and English online question-answering communities

This paper compares 12 representative Chinese and English online questionanswering communities(Q&A communities) based on their basic functions, interactive modes, and customized services. An empirical experiment from a comparative perspective was also conducted on them by using 12 questions representing for four types of questions,which are assigned evenly to three different subject fields so as to examine the task performance of these 12 selected online Q&A communities. Our goal was to evaluate those online Q&A communities in terms of their quality and efficiency for answering questions posed to them. It was hoped that our empirical research would yield greater understanding and insights to the working intricacy of these online Q&A communities and hence their possible further improvement.

Development of a Best Answer Recommendation Model in a Community Question Answering (CQA) System

In this work, a best answer recommendation model is proposed for a Question Answering (QA) system. A Community Question Answering System was subsequently developed based on the model. The system applies Brouwer Fixed Point Theorem to prove the existence of the desired voter scoring function and Normalized Google Distance (NGD) to show closeness between words before an answer is suggested to users. Answers are ranked according to their Fixed-Point Score (FPS) for each question. Thereafter, the highest scored answer is chosen as the FPS Best Answer (BA). For each question asked by user, the system applies NGD to check if similar or related questions with the best answer had been asked and stored in the database. When similar or related questions with the best answer are not found in the database, Brouwer Fixed point is used to calculate the best answer from the pool of answers on a question then the best answer is stored in the NGD data-table for recommendation purpose. The system was implemented using PHP scripting language, MySQL for database management, JQuery, and Apache. The system was evaluated using standard metrics: Reciprocal Rank, Mean Reciprocal Rank (MRR) and Discounted Cumulative Gain (DCG). The system eliminated longer waiting time faced by askers in a community question answering system. The developed system can be used for research and learning purposes.

A Survey on Expert Recommendation in Community Question Answering

Community question answering （CQA） represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify the questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.