LKMT:Linguistics Knowledge-Driven Multi-Task Neural Machine Translation for Urdu and English

Thanks to the strong representation capability of pre-trained language models,supervised machine translation models have achieved outstanding performance.However,the performances of these models drop sharply when the scale of the parallel training corpus is limited.Considering the pre-trained language model has a strong ability for monolingual representation,it is the key challenge for machine translation to construct the in-depth relationship between the source and target language by injecting the lexical and syntactic information into pre-trained language models.To alleviate the dependence on the parallel corpus,we propose a Linguistics Knowledge-Driven MultiTask(LKMT)approach to inject part-of-speech and syntactic knowledge into pre-trained models,thus enhancing the machine translation performance.On the one hand,we integrate part-of-speech and dependency labels into the embedding layer and exploit large-scale monolingual corpus to update all parameters of pre-trained language models,thus ensuring the updated language model contains potential lexical and syntactic information.On the other hand,we leverage an extra self-attention layer to explicitly inject linguistic knowledge into the pre-trained language model-enhanced machine translation model.Experiments on the benchmark dataset show that our proposed LKMT approach improves the Urdu-English translation accuracy by 1.97 points and the English-Urdu translation accuracy by 2.42 points,highlighting the effectiveness of our LKMT framework.Detailed ablation experiments confirm the positive impact of part-of-speech and dependency parsing on machine translation.

Exploring Nominalization in English Abstracts of Applied Linguistics Research Articles:A Comparative Study Between Chinese and International Journals

Researchers around the world strive to communicate new knowledge,primarily via publication,with the abstract being crucial in conveying core insights.Previous research has generally analyzed the discourse features of abstracts from a macro perspective and often employed either outdated texts,such as those over a decade old,or papers written by authors with lower English academic writing proficiency as research material.In this study,we analyzed forty abstracts from leading journals in applied linguistics,evenly split between Chinese and international journals.It revealed that the use of nominalization in abstracts by Chinese and international scholars showed similarities due to the universal academic requirement for conciseness.However,due to cultural and educational differences,each group differed in their respective language choices and nominalization usage.By analyzing the application of nominalization in different cultural contexts,the results of our study offered practical suggestions for crafting abstracts that effectively convey information,thereby,contributing to the broader academic community.

Review of Accessing Academic Discourse:Systemic Functional Linguistics and Legitimation Code Theory

Accessing Academic Discourse:Systemic Functional Linguistics and Legitimation Code Theory,was edited by J.R.Martin,Karl Maton and Y.J.Doran and published by Routledge in 2020.This volume explores the dialogue with Legitimation Code Theory(LCT)from the viewpoint of systemic functional linguistics(SFL).“Systemic functional linguistics(SFL)is a long-established and widely known approach to understanding language.Legitimation Code Theory(LCT)is a younger and rapidly growing approach to exploring and shaping knowledge practices”(p.1).They have become widely used to access academic discourse by a growing number of systemic scholars and educators.

Cultural Linguistics and Intercultural Communication

The relationship between language and culture is the core issue in intercultural communication.For native language learners,their native language and local culture are in perfect harmony;while for foreign language learners,they often unconsciously separate the two,resulting in the phenomenon of“half-cooked rice”in foreign language education.This phenomenon makes us reflect on the purpose,content,and methods of foreign language education and seek theoretical guidance.Foreign language teaching aims to improve intercultural competencies,so it must face language and culture in intercultural communication.As a branch of cultural linguistics with multidisciplinary origins,intercultural linguistics focuses on the relationship between language and culture in intercultural communication.In the process of exploring how to apply linguistic theories in second language acquisition,it shows that intercultural linguistics not only has important enlightenment and application value,but also needs further studies.

The Application of Embodied Cognitive Linguistics in the English Translation of Two-Part Allegorical Saying—An Analysis of Examples From A Dream of Red Mansions

As a kind of widely circulated folk language,two-part allegorical saying(“xiehouyu”)carries the wisdom of the Chinese people and is characterized with humor and vivid imagery.Taking the two English translation versions of A Dream of Red Mansions as examples,this paper analyzes the English translation of two-part allegorical saying based on the core principle of Embodied Cognitive Linguistics(ECL),“Reality-Cognition-Language”,and explores how the English translation of two-part allegorical saying reflects the three dimensions of ECL.

The Application and Effect Evaluation of Corpus Linguistics in English Teaching

This paper explores the application and effect evaluation of corpus linguistics in English teaching.It first introduces the basic concepts and development history of corpus linguistics,then analyzes its connection with language teaching theories,discussing the advantages and challenges of using corpora in language teaching.Next,it delves into the methods and technologies for building and utilizing corpora,as well as their role in developing teaching resources.Lastly,within the framework and methods of teaching effect evaluation,specific application cases of corpus linguistics in teaching design are analyzed,and their effects are assessed.Recommendations for teaching improvement and future development directions are also proposed.

A Study on the Utilization of Applied Linguistics in Contemporary English Teaching

In the context of globalization,the importance of teaching English as a common language in the world becomes increasingly apparent.Applied Linguistics belongs to one of the sub-disciplines of linguistics,which plays an important role in contemporary English teaching.The knowledge of applied linguistics can help students learn the grammar,semantics,vocabulary,writing,and other contents in English,deepen their knowledge and understanding of English,and enhance the effectiveness of English teaching.This paper mainly describes the important role played by English linguistics in English teaching and focuses on its utilization strategies in the hope of providing references on this topic.

A Study of English Grammar Teaching and Cultural Introduction from the Perspective of Applied Linguistics

Grammar teaching is the main content of university English teaching,and the quality of teaching will affect the effect of university talent cultivation.Based on the perspective of applied linguistics,it is crucial to use cultural introduction teaching for English grammar teaching.This paper will combine the basic concepts of applied linguistics and cultural introduction teaching,analyze the role of using the cultural introduction teaching method to innovate grammar teaching mode under the perspective of applied linguistics,and put forward a scientific English grammar teaching strategy with the goal of improving teaching quality.

Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.

Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media

Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions.The number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a betterway.Irony and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion mining.For the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web content.Therefore,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social media.The presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social media.To attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature extraction.For irony detection and classification,the DBN model is exploited in this work.At last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)algorithm.The experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark dataset.The simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.

Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection

Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.

Chinese Journal of Applied Linguistics Call for Papers

The journal Chinese Journal of Applied Linguistics (CJAL) is the official journal of China English Language Education Association (CELEA),the Chinese affiliate of AILA (International Association of Applied Linguistics).It was initiated in 1978 as the only English language journal in China in the area of applied linguistics.Prof.Wen Qiufang from Beijing Foreign Studies University,former President of CELEA,serves as the current editor-in-chief.

Dart Games Optimizer with Deep Learning-Based Computational Linguistics Named Entity Recognition

Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.

Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems.The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights.The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users.The Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the data.The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)model.The presented ALTS-MLODL technique aims to summarize the text documents in the English language.To accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of features.Next,the MLO algorithm is used for the effectual selection of the extracted features.For the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter optimizer.The exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization results.To validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were conducted.The experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.

Critical Discourse Analysis Based on Halliday’s Systemic Functional Linguistics: Taking The Economist’s First Commentation on the End of China’s Zero-COVID Policy as an Example

Based on Halliday’s systemic functional grammar,especially the ideational function,this research aims at disclosing the hidden ideologies and values of the seemingly objective news reports on China’s COVID-19 policies in The Economist.Transitivity,voice,and nominalization are the major analytical subjects.After China lifted the zero-COVID policy,western media began criticizing China’s lack of data sharing,with some misinformation and misleading reports.The denouncement of inertness and reluctance to fight against the pandemic disclaim the Chinese government’s efforts and depreciate China’s image.China is portrayed as the villain and destroyer of people’s health worldwide.Meanwhile,they also hold a hesitant attitude toward China’s diplomacy.The re-engaging with foreign countries and travel restrictions have been described as imprudent and rushed actions.They also consider China as the fuse of contradiction in the United Nations.What is overt is their view of breaking up China.

Linguistic landscape and its impacts on place-making in mountain destination:A case of Mogan Mountain Town of Huzhou City,China

The place and placeness are believed to be a crucial perspective for comprehending the transformative dynamics of tourist destinations in the context of globalization and the rapid development of the tourism industry.As the material carrier of language,linguistic landscape is an intermediary for people to endow emotions and personalities into the place,becoming an effective path for exploring the place-making in a certain area.Mogan Mountain is a famous national tourist resort,which is located in Mogan Mountain Town,Deqing County,Huzhou City,China.This is a good case area for studying the relationship between linguistic landscape and placemaking in tourist destinations.There are two theories that form the theoretical foundation of the study,namely the place and the geosemiotics.Firstly,the results showed the overall style of the linguistic landscape.Multilingual labelling(52.7%),official signs(55.3%),and standardized Chinese characters predominantly shaped the linguistic landscape,with English and other languages being complementary.Secondly,from the perspective of three dimensions of the geosemiotics framework,such as inscription,language preference,and placement position,we conduct a comprehensive semiotic analysis of the linguistic landscape,which encompasses diverse facets such as fonts,materials,shifts in state,text vectors,and symmetrical arrangements.Finally,we explore the influence of linguistic landscapes on the place-making of Mogan Mountain through the dimensions of location,locale,and sense of place.The information on linguistic landscapes indicates the geographical location.These linguistic landscapes play an important role in constructing the language order,nostalgic atmosphere,exotic atmosphere,and elegant and tasteful lifestyle of the public space in Mogan Mountain.Readers of linguistic landscapes,including tourists and residents,generate and identify with the unique sense of place in Mogan Mountain.These linguistic landscapes construct the destination placeness of Mogan Mountain that combines both foreign and nostalgic styles,and the integration of Chinese and Western cultures.It will help promote the image positioning of tourism destinations and have practical guiding significance for the tourism planning,landscape-making,language management,and other aspects of tourism destinations.

Agricultural Investment Project Decisions Based on an Interactive Preference Disaggregation Model Considering Inconsistency

Agricultural investment project selection is a complex multi-criteria decision-making problem,as agricultural projects are easily influenced by various risk factors,and the evaluation information provided by decisionmakers usually involves uncertainty and inconsistency.Existing literature primarily employed direct preference elicitation methods to address such issues,necessitating a great cognitive effort on the part of decision-makers during evaluation,specifically,determining the weights of criteria.In this study,we propose an indirect preference elicitation method,known as a preference disaggregation method,to learn decision-maker preference models fromdecision examples.To enhance evaluation ease,decision-makers merely need to compare pairs of alternatives with which they are familiar,also known as reference alternatives.Probabilistic linguistic preference relations are employed to account for the presence of incomplete and uncertain information in such pairwise comparisons.To address the inconsistency among a group of decision-makers,we develop a pair of 0-1mixed integer programming models that consider both the semantics of linguistic terms and the belief degrees of decision-makers.Finally,we conduct a case study and comparative analysis.Results reveal the effectiveness of the proposed model in solving agricultural investment project selection problems with uncertain and inconsistent decision information.

Heterogeneous information fusion recognition method based on belief rule structure

To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion,but the expert knowledge is not fully utilized,a heterogeneous informa-tion fusion recognition method based on belief rule structure is proposed.By defining the continuous probabilistic hesitation fuzzy linguistic term sets(CPHFLTS)and establishing CPHFLTS distance measure,the belief rule base of the relationship between feature space and category space is constructed through information integration,and the evidence reasoning of the input samples is carried out.The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition.Compared with the other methods,the proposed method has a higher correct recognition rate under different noise levels.

An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods

Hydrogen is the new age alternative energy source to combat energy demand and climate change.Storage of hydrogen is vital for a nation’s growth.Works of literature provide different methods for storing the produced hydrogen,and the rational selection of a viable method is crucial for promoting sustainability and green practices.Typically,hydrogen storage is associated with diverse sustainable and circular economy(SCE)criteria.As a result,the authors consider the situation a multi-criteria decision-making(MCDM)problem.Studies infer that previous models for hydrogen storage method(HSM)selection(i)do not consider preferences in the natural language form;(ii)weights of experts are not methodically determined;(iii)hesitation of experts during criteria weight assessment is not effectively explored;and(iv)three-stage solution of a suitable selection of HSM is unexplored.Driven by these gaps,in this paper,authors put forward a new integrated framework,which considers double hierarchy linguistic information for rating,criteria importance through inter-criteria correlation(CRITIC)for expert weight calculation,evidence-based Bayesian method for criteria weight estimation,and combined compromise solution(CoCoSo)for ranking HSMs.The applicability of the developed framework is testified by using a case example of HSM selection in India.Sensitivity and comparative analysis reveal the merits and limitations of the developed framework.

Aims & Scope

Contemporary Social Sciences aims to present the most outstanding research and achievements in the field of social sciences in China.The scopes of research fields for consideration include,but not limited to political science,economics,literature,linguistics,journalism and communication,education,sociology,philosophy,history,law,and interdisciplinary research.Contemporary Social Sciences enhances the international discourse of China and seeks to promote communication and cooperation between scholars from China and the rest of the world.