Search

Copyright of Eurasian Journal of Philology: Science & Education is the property of Al-Farabi Kazakh National University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of a transformer based chatbot-like architecture for task classification with a focus on natural human interaction with a machine as opposed to interfaces, code, or formal commands. The intelligent system augments human-sourced data via artificial paraphrasing in order to generate a large set of training data for further classical, attention, and language transformation-based learning approaches for Natural Language Processing (NLP). Human beings are asked to paraphrase commands and questions for task identification for further execution of algorithms as skills. The commands and questions are split into training and validation sets. A total of 483 responses were recorded. Secondly, the training set is paraphrased by the T5 model in order to augment it with further data. Seven state-of-the-art transformer-based text classification algorithms (BERT, DistilBERT, RoBERTa, DistilRoBERTa, XLM, XLM-RoBERTa, and XLNet) are benchmarked for both sets after fine-tuning on the training data for two epochs. We find that all models are improved when training data is augmented by the T5 model, with an average increase of classification accuracy by 4.01%. The best result was the RoBERTa model trained on T5 augmented data which achieved 98.96% classification accuracy. Finally, we found that an ensemble of the five best-performing transformer models via Logistic Regression of output label predictions led to an accuracy of 99.59% on the dataset of human responses. A highly-performing model allows the intelligent system to interpret human commands at the social-interaction level through a chatbot-like interface (e.g. "Robot, can we have a conversation?") and allows for better accessibility to AI by non-technical users. [ABSTRACT FROM AUTHOR]

Introduction. Based on the analysis of the transformation processes of the linguistic culture of the Urals Mari, the article considers how various types of transformations can give a culture a new potential for its development or have a harmful influence on its foundations. With the prognostic goal the work makes an attempt to outline the structure of the model of language transformations, which is tested on the example of the language culture of the Urals Mari. Materials and Methods. In the framework of cultural interpretation, interviewing, survey and sociological description are applied. Hermeneutical and lingvocultural methods are also used. Results and Discussion. Historically, it was recorded that the changing of the language culture of the Ural Mari was influenced by proximity to other nations, trading activities, borrowing in material culture. The formation of the Mari alphabet gave a fresh impetus to the development of ethnocultural education. Thus, ethnocultural identity was expressed in writing. The language of the ethnos, perceived visually and thus fixing the achievements of culture, contributed to the awareness of the community of the ethos. Analyzing such borrowings, the author comes to the conclusion about positive transformational processes. Negative transformation processes are associated by the author with the mass culture and the loss of ethnocultural values. Mass culture comes along with the loss of traditional types of employment, together with the termination of the study of the language of the ethnic group in the education system. Conclusion. The author comes to the conclusion that the developing the regions of the Urals Mari festival and tourist activities carried out in the language of the super ethnos cannot influence the leveling of the value foundations of culture. First of all, it is necessary to level out the processes that negatively affect the language and culture of the ethnic group.

This article outlines the severity of historical injustices in the South African Higher Education system and introduces the key concepts that underpin the argument. Reporting on a series of interviews will be provided to demonstrate how these injustices continue to be perpetuated. “The subject of dissension” regarding “decolonisation” insinuates that the universities implicated in the study are colonial; “they are an integral part” of an empire that has a role in preserving fixed values for the wellbeing of an elite. This elite has the racial connotation of being white. Whiteness is an avenue for preserving their opulence and echelon within an ideological, theocratic capitalist modus operandi. This wrangle is disingenuous and ahistorical. Despite the revised language policy that seeks to promote multilingualism and facilitate inclusivity, social cohesion and meaningful participation by all students through focusing on previously marginalised languages, the associated institutions’ curricula, reproduce the community’s wider inequalities. [ABSTRACT FROM AUTHOR]

Despite the many integration tools proposed for mapping between OWL ontologies and the object-oriented paradigm, developers are still reluctant to incorporate ontologies into their code repositories. In this paper we survey existing approaches for OWL-to-OOP mapping trying to identify reasons for this shy adoption of ontologies among conventional software developers. We present a classification of the surveyed approaches and tools based on their technical characteristics and their resulting artifacts. We discuss further potential reasons beyond what have been addressed in the literature before finally providing our own reflection and outlook. [ABSTRACT FROM AUTHOR]

The discursive use of language in legal contexts is multifaceted and complex. First, it begins with the chain of oral narrative structures (victim, witness, suspect accounts) and written texts (confessions, notes, depositions, letters, Internet documents, email, tweets, telephone records) used in a court of law. The transcription of oral communiqués is a potentially transformative phase of evidence production that may be flawed (ambiguity, error, mistakes, gaps, misrepresentation). Second, it includes or/excludes the textual and narrative representation of nonverbal communication (kinesics, paralanguage, proxemics) and how it is recorded (transcripts, graphics, video, enactments). At each stage of the legal process, interpretation of narrative structures and texts plays a key role in the ultimate meaning of verbal and nonverbal communication. Third, it concerns interrogation strategies and tactics in preliminary evidence gathering as well as in the courtroom. Fourth, it involves translation of documents written in another language or interpretation of oral statements in another language to the language of the courtroom. [ABSTRACT FROM AUTHOR]