Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

Research on Foreign Language Talent Cultivation Mode Based on the Concept of Language Security

Language security is an important part of national security,and in the current complex international environment,the issue of language security is becoming more and more prominent and has become an unignorable part of national security strategy.As the bridge and link of international communication,foreign language talents’language skills and the level of national security awareness directly affect our country’s international image and the effect of international communication.Therefore,the establishment of a foreign language talent cultivation mode based on the concept of language security is not only an important way to improve the quality of foreign language education but also a realistic need to safeguard national security and promote international exchanges.Starting from the influence of the language security concept on foreign language talent cultivation,this paper analyzes the foreign language talent cultivation mode based on the language security concept,with a view to providing new ideas and methods for the development of foreign language education.

NewBee: Context-Free Grammar (CFG) of a New Programming Language for Novice Programmers

Learning programming and using programming languages are the essential aspects of computer science education.Students use programming languages to write their programs.These computer programs(students or practitioners written)make computers artificially intelligent and perform the tasks needed by the users.Without these programs,the computer may be visioned as a pointless machine.As the premise of writing programs is situated with specific programming languages,enormous efforts have been made to develop and create programming languages.However,each program-ming language is domain-specific and has its nuances,syntax and seman-tics,with specific pros and cons.These language-specific details,including syntax and semantics,are significant hurdles for novice programmers.Also,the instructors of introductory programming courses find these language specificities as the biggest hurdle in students learning,where more focus is on syntax than logic development and actual implementation of the program.Considering the conceptual difficulty of programming languages and novice students’struggles with the language syntax,this paper describes the design and development of a Context-Free Grammar(CFG)of a programming language for the novice,newcomers and students who do not have computer science as their major.Due to its syntax proximity to daily conversations,this paper hypothesizes that this language will be easy to use and understand by novice programmers.This paper systematically designed the language by identifying themes from various existing programming languages(e.g.,C,Python).Additionally,this paper surveyed computer science experts from industry and academia,where experts self-reported their satisfaction with the newly designed language.The results indicate that 93%of the experts reported satisfaction with the NewBee for novice,newcomer and non-Computer Sci-ence(CS)major students.

Recent Advances on Deep Learning for Sign Language Recognition

Sign language,a visual-gestural language used by the deaf and hard-of-hearing community,plays a crucial role in facilitating communication and promoting inclusivity.Sign language recognition(SLR),the process of automatically recognizing and interpreting sign language gestures,has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world.The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR.This paper presents a comprehensive and up-to-date analysis of the advancements,challenges,and opportunities in deep learning-based sign language recognition,focusing on the past five years of research.We explore various aspects of SLR,including sign data acquisition technologies,sign language datasets,evaluation methods,and different types of neural networks.Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN)have shown promising results in fingerspelling and isolated sign recognition.However,the continuous nature of sign language poses challenges,leading to the exploration of advanced neural network models such as the Transformer model for continuous sign language recognition(CSLR).Despite significant advancements,several challenges remain in the field of SLR.These challenges include expanding sign language datasets,achieving user independence in recognition systems,exploring different input modalities,effectively fusing features,modeling co-articulation,and improving semantic and syntactic understanding.Additionally,developing lightweight network architectures for mobile applications is crucial for practical implementation.By addressing these challenges,we can further advance the field of deep learning for sign language recognition and improve communication for the hearing-impaired community.

Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.

Impact of transcranial electrical stimulation on serum neurotrophic factors and language function in patients with speech disorders

BACKGROUND Speech disorders have a substantial impact on communication abilities and quality of life.Traditional treatments such as speech and psychological therapies frequently demonstrate limited effectiveness and patient compliance.Transcranial electrical stimulation(TES)has emerged as a promising non-invasive treatment to improve neurological functions.However,its effectiveness in enhancing language functions and serum neurofactor levels in individuals with speech disorders requires further investigation.AIM To investigate the impact of TES in conjunction with standard therapies on serum neurotrophic factor levels and language function in patients with speech disorders.METHODS In a controlled study spanning from March 2019 to November 2021,81 patients with speech disorders were divided into a control group(n=40)receiving standard speech stimulation and psychological intervention,and an observation group(n=41)receiving additional TES.The study assessed serum levels of ciliary neurotrophic factor(CNTF),glial cell-derived neurotrophic factor(GDNF),brainderived neurotrophic factor(BDNF),and nerve growth factor(NGF),as well as evaluations of motor function,language function,and development quotient scores.RESULTS After 3 wk of intervention,the observation group exhibited significantly higher serum levels of CNTF,GDNF,BDNF,and NGF compared to the control group.Moreover,improvements were noted in motor function,cognitive function,language skills,physical abilities,and overall development quotient scores.It is worth mentioning that the observation group also displayed superior perfor CONCLUSION This retrospective study concluded that TES combined with traditional speech and psychotherapy can effectively increase the levels of neurokines in the blood and enhance language function in patients with speech disorders.These results provide a promising avenue for integrating TES into standard treatment methods for speech disorders.

Potential use of large language models for mitigating students’problematic social media use:ChatGPT as an example

The problematic use of social media has numerous negative impacts on individuals'daily lives,interpersonal relationships,physical and mental health,and more.Currently,there are few methods and tools to alleviate problematic social media,and their potential is yet to be fully realized.Emerging large language models(LLMs)are becoming increasingly popular for providing information and assistance to people and are being applied in many aspects of life.In mitigating problematic social media use,LLMs such as ChatGPT can play a positive role by serving as conversational partners and outlets for users,providing personalized information and resources,monitoring and intervening in problematic social media use,and more.In this process,we should recognize both the enormous potential and endless possibilities of LLMs such as ChatGPT,leveraging their advantages to better address problematic social media use,while also acknowledging the limitations and potential pitfalls of ChatGPT technology,such as errors,limitations in issue resolution,privacy and security concerns,and potential overreliance.When we leverage the advantages of LLMs to address issues in social media usage,we must adopt a cautious and ethical approach,being vigilant of the potential adverse effects that LLMs may have in addressing problematic social media use to better harness technology to serve individuals and society.

Embracing different languages and local differences:Coconstructive patient simulation strengthens host countries’clinical training in psychiatry

BACKGROUND Global education in psychiatry is heavily influenced by knowledge from Western,high-income countries,which obscures local voices and expertise.AIM To adapt a human simulation model to psychiatric education in a context that is specific to local languages and cultures.METHODS We conducted an observational study consisting of six human simulation sessions with standardized patients from two host countries,speaking their native languages,and following an adaptation of the co-constructive patient simulation(CCPS)model.As local faculty became increasingly familiar with the CCPS approach,they took on the role of facilitators—in their country’s native language.RESULTS Fifty-three learners participated:19 child and adolescent psychiatry trainees and 3 faculty members in Türkiye(as a group that met online during 3 consecutive months);and 24 trainees and 7 faculty in Israel(divided into 3 groups,in parallel in-person sessions during a single training day).Each of the six cases reflected local realities and clinical challenges,and was associated with specific learning goals identified by each case-writing trainee.CONCLUSION Human simulation has not been fully incorporated into psychiatric education:The creation of immersive clinical experiences and the strengthening of reflective practice are two areas ripe for development.Our adaptations of CCPS can also strengthen local and regional networks and psychiatric communities of practice.Finally,the model can help question and press against hegemonies in psychiatric training that overshadow local expertise.

Let’s Talk LGBTQIA+:Strategies and Activities for Teaching Diversity and Inclusion in the Foreign Language Classroom at the College Level

In this presentation,the author explores strategies and activities that create a welcoming and nurturing environment for LGBTQIA+foreign language students at the college level in the United States.Indeed,as it is commonly stated,there is a clear lack of visibility,acceptance/tolerance,and inclusion of LGBTQIA+voices(active and proactive)in foreign language teaching and learning(pedagogical material as well as in-class attitudes and delivery).The reasons for this deficiency are diverse since they cover not only outward discrimination against and/or indifference(passive-aggressive behavior)toward the LGBTQIA+community and its plight,but they also illustrate the sheer,physical inexistence of quality material that can be used to create,present,and foster a welcoming and all-encompassing classroom environment that addresses the diverse spectrum of the LGBTQIA+community while its members try to learn a foreign language and(positively)negotiate how to express their very identity in their new language of choice.Moreover,the scant material that is available in the United States as well as in other Western countries where the LGBTQIA+community by and large is accepted-at least from the legal point of view-hardly represents and covers the subject matter in a satisfactory way.To address this gap,the author will provide some examples in European Portuguese(EP)and Italian(IT),followed by a courtesy translation in English,to illustrate some positive activities and strategies for including LGBTQIA+-related subject matter in the foreign language classroom at the college level.It is his desire that instructors of other foreign languages duplicate in their respective vernaculars the strategies and activities herein presented in order to create a welcoming and safe classroom environment that embraces the LGBTQIA+community as it goes through the enjoyable journey of learning a new language and culture.

Researching Creativity in Second Language Acquisition:Book Review

In the realm of psychology and language acquisition,the exploration of“individual differences”-encompassing attributes such as personality traits,motivation,and language aptitude-has long been a focal point of scholarly investigation.Within this sphere,the book“Researching Creativity in Second Language Acquisition”by Ashleigh Pipes offers a fresh perspective on the role of creativity as a significant individual difference in Second Language Acquisition(SLA).Positioned against the backdrop of extensive empirical research on individual differences,this book stands out by focusing on the often overlooked aspect of creativity,which Pipes argues,holds paramount importance in SLA.

A Robust Model for Translating Arabic Sign Language into Spoken Arabic Using Deep Learning

This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken Arabic.The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models.The image-based translation method maps sign language gestures to corre-sponding letters or words using distance measures and classification as a machine learning technique.The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs,with a translation accuracy of 93.7%.This research makes a significant contribution to the field of ATSL.It offers a practical solution for improving communication for individuals with special needs,such as the deaf and mute community.This work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.

Translation Strategies of Ambiguity in English Language and Literature

The translation of English language and literary works has always been crucial for cross-cultural communication.However,a key challenge in translating such works is the accurate communication of ambiguity,which refers to expressions deliberately used in English texts with unclear meanings.These expressions are often poetic and carry deep symbols and implications,adding a unique charm to literary works.This article explores the manifestations of ambiguity in English language and literature and the translation strategies that can be employed to optimize the translation of ambiguity in the English language and literature.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select the appropriate language phrase set according to their own situation,give the preference information of the weight of each key indicator,and then transform the multi-granularity language information through consistency.On this basis,the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator.Subsequently,the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence,and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme.Lastly,the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.

Research on the Status Quo and Improvement Needs of Foreign Language Teachers’ Teaching Competence in Curriculum-Based Ideological Education in Xinjiang Colleges and Universities

This study investigated the status quo and further improvement needs of foreign language teachers’teaching competence in curriculum-based ideological education(CIE)in Xinjiang colleges and universities.The results show that the teachers’teaching competence in CIE is generally good,with the“cultivating quality”being the highest and the“scientific research quality”the lowest;there still exist several problems,such as teachers’lack of theoretical knowledge in CIE,lack of teaching resources,and insufficient abilities in teaching design and research.It is hoped that with the support and efforts from university administrations,teachers can improve their teaching competence in CIE through various approaches adopted and multiple measures taken,thus optimizing their teaching effectiveness,promoting the construction of CIE in Xinjiang colleges and universities in the new era,and contributing to Xinjiang’s high-quality development.

Reading Loss in Arabic Language During COVID-19 in the UAE and Proposed Solutions:The Perspectives of Primary-Grade Arabic Language Teachers

The COVID-19 pandemic caused significant disruptions in the field of education worldwide,including in the United Arab Emirates.Teachers and students had to adapt to remote learning and virtual classrooms,leading to various challenges in maintaining educational standards.The sudden transition to remote teaching could have a negative impact on students’reading abilities,especially in the Arabic language.To gain insight into the unique challenges encountered by Arabic language teachers in the UAE,a survey was conducted to explore their assessment of teaching quality,student-teacher interaction,and learning outcomes amidst the COVID-19 pandemic.The results of the survey revealed a significant decline of student reading abilities and identified several major issues in online Arabic language teaching.These issues included limited interaction between students and teachers,challenges in monitoring students’class participation and performance,and challenges in effectively assessing students’reading skills.The results also demonstrated some other challenges faced by Arabic language teachers,including a lack of preparedness,a lack of subscription to relevant platforms,and a lack of resources for online learning.Several solutions to these challenges are proposed,including reevaluating the balance between depth and breadth in the curriculum,integrating language skills into the curriculum more effectively,providing more comprehensive teacher professional development,implementing student grouping strategies,utilizing retired and expert teachers in specific content areas,allocating time for interventions,and improving support from both teachers and parents to ensure the quality of online learning.

Knowledge Graph Analysis of International Chinese Language Textbooks Based on CiteSpace

Drawing upon relevant papers from Chinese core journals and CSSCI source journals in the CNKI China Academic Journals Full-Text Database spanning from 1992 to 2023,this study utilizes CiteSpace as a research tool to visually analyze the knowledge graph structure of research on international Chinese language textbooks in China.The study maps out the publication timeline,authors,institutions,collaborative networks,and keywords pertaining to research on international Chinese language textbooks.The findings indicate that research on international Chinese language textbooks commenced early and continues to maintain a certain level of research interest,yet lacks sufficient research output.Research institutions predominantly reside in universities and publishing groups specializing in language or education,with collaboration between institutions being relatively scarce.High-frequency keywords in recent research on international Chinese language textbooks include“Chinese language textbooks for the Foreigners,”“Chinese language textbooks,”“Teaching Chinese Language for the Foreigners,”“Textbook compilation,”“International Chinese Language Education and Localization,”which reflect a diversified research perspective with interdisciplinary trends.Future research priorities encompass research on localization,customization of textbooks,and evaluation of textbooks which represent forefront directions of research.