Language, writing, and social (inter)action: An analysis of text-based chats in Macedonian and English

The purpose of this study is to investigate the text-based chatting practices of a particular community of native Macedonian speakers who chat both in Macedonian and in English (as their foreign language). Much research in computer-mediated communication (CMC) over the last decade has been done in English as L1. Some of the few studies which explored CMC cross-linguistically include the comparison of French vs. English (Werry, 1996), Japanese vs. English (Nishimura, 2003b), Spanish vs. English (del-Teso-Craviotto, 2006), Serbian vs. English (Radic, 2007) and Turkish vs. English (Savas, 2010). In these studies, a number of different language features (e.g., orthography, code switching) and functions (e.g., representation of gender) common to TBC have been analyzed, but none has explored in-depth the use of language as social action in online text-based interactions. Data collected from surveys, text-based chats, and interviews were analyzed qualitatively and quantitatively using methods and concepts borrowed from discourse analysis, conversation analysis, systemic functional linguistics and communication accommodation theory. Seventy text-based chats in Macedonian and English from seven native Macedonian speakers, who form an intact group, were collected over a period of four months. By investigating linguistic elements, extralinguistic phenomena (e.g., emoticons, typographic forms such as LOL), and contextual phenomena (e.g., appraisal, limitations of the medium) in the text-based chats of my participants, and by conducting follow-up text-based interviews regarding their individual chatting practices, this study has explored how all these phenomena are used for performing social action in two languages. Text-based chat was also found to be a convenient medium for participants to co-position in various ways while carefully accommodating to various contextual factors

Exploring the relationship between electronic literacy and heritage language maintenance

This paper focuses on the electronic literacy practices of two Korean-American heritage language learners who manage Korean weblogs. Online users deliberately alter standard forms of written language and play with symbols, characters, and words to economize typing effort, mimic oral language, or convey qualities of their linguistic identity such as gender, age, and emotional states. However, little is known about the impact of computer-mediated nonstandard language use on heritage learners’ linguistic development. Through in-depth case studies of two siblings, the study examines the linguistic and pragmatic practices of these learners online and the perceived effects of non-standard forms of computer-mediated language on their heritage language development and maintenance. The data show that electronic literacy practices provide authentic opportunities to use the language and support the development of a social network of Korean speakers, which results in greater sociopsychological attachment to the Korean language and culture. The informants report that the deviant language forms found in e-texts enable them to engage in online interactions without the pressures of having to spell the words correctly. However, they express frustrations in not being able to distinguish between correct and non-standard forms of the language, which appear to be affecting their offline language use

深層学習に基づく感情会話分析に関する研究

Owning the capability to express specific emotions by a chatbot during a conversation is one of the key parts of artificial intelligence, which has an intuitive and quantifiable impact on the improvement of chatbot’s usability and user satisfaction. Enabling machines to emotion recognition in conversation is challenging, mainly because the information in human dialogue innately conveys emotions by long-term experience, abundant knowledge, context, and the intricate patterns between the affective states. Recently, many studies on neural emotional conversational models have been conducted. However, enabling the chatbot to control what kind of emotion to respond to upon its own characters in conversation is still underexplored. At this stage, people are no longer satisfied with using a dialogue system to solve specific tasks, and are more eager to achieve spiritual communication. In the chat process, if the robot can perceive the user's emotions and can accurately process them, it can greatly enrich the content of the dialogue and make the user empathize. In the process of emotional dialogue, our ultimate goal is to make the machine understand human emotions and give matching responses. Based on these two points, this thesis explores and in-depth emotion recognition in conversation task and emotional dialogue generation task. In the past few years, although considerable progress has been made in emotional research in dialogue, there are still some difficulties and challenges due to the complex nature of human emotions. The key contributions in this thesis are summarized as below: (1) Researchers have paid more attention to enhancing natural language models with knowledge graphs these days, since knowledge graph has gained a lot of systematic knowledge. A large number of studies had shown that the introduction of external commonsense knowledge is very helpful to improve the characteristic information. We address the task of emotion recognition in conversations using external knowledge to enhance semantics. In this work, we employ an external knowledge graph ATOMIC to extract the knowledge sources. We proposed KES model, a new framework that incorporates different elements of external knowledge and conversational semantic role labeling, where build upon them to learn interactions between interlocutors participating in a conversation. The conversation is a sequence of coherent and orderly discourses. For neural networks, the capture of long-range context information is a weakness. We adopt Transformer a structure composed of self-attention and feed forward neural network, instead of the traditional RNN model, aiming at capturing remote context information. We design a self-attention layer specialized for enhanced semantic text features with external commonsense knowledge. Then, two different networks composed of LSTM are responsible for tracking individual internal state and context external state. In addition, the proposed model has experimented on three datasets in emotion detection in conversation. The experimental results show that our model outperforms the state-of-the-art approaches on most of the tested datasets. (2) We proposed an emotional dialogue model based on Seq2Seq, which is improved from three aspects: model input, encoder structure, and decoder structure, so that the model can generate responses with rich emotions, diversity, and context. In terms of model input, emotional information and location information are added based on word vectors. In terms of the encoder, the proposed model first encodes the current input and sentence sentiment to generate a semantic vector, and additionally encodes the context and sentence sentiment to generate a context vector, adding contextual information while ensuring the independence of the current input. On the decoder side, attention is used to calculate the weights of the two semantic vectors separately and then decode, to fully integrate the local emotional semantic information and the global emotional semantic information. We used seven objective evaluation indicators to evaluate the model's generation results, context similarity, response diversity, and emotional response. Experimental results show that the model can generate diverse responses with rich sentiment, contextual associations

Machine Learning from Casual Conversation

Human social learning is an effective process that has inspired many existing machine learning techniques, such as learning from observation and learning by demonstration. In this dissertation, we introduce another form of social learning, Learning from a Casual Conversation (LCC). LCC is an open-ended machine learning system in which an artificially intelligent agent learns from an extended dialog with a human. Our system enables the agent to incorporate changes into its knowledge base, based on the human\u27s conversational text input. This system emulates how humans learn from each other through a dialog. LCC closes the gap in the current research that is focused on teaching specific tasks to computer agents. Furthermore, LCC aims to provide an easy way to enhance the knowledge of the system without requiring the involvement of a programmer. This system does not require the user to enter specific information; instead, the user can chat naturally with the agent. LCC identifies the inputs that contain information relevant to its knowledge base in the learning process. LCC\u27s architecture consists of multiple sub-systems combined to perform the task. Its learning component can add new knowledge to existing information in the knowledge base, confirm existing information, and/or update existing information found to be related to the user input. %The test results indicate that the prototype was successful in learning from a conversation. The LCC system functionality was assessed using different evaluation methods. This includes tests performed by the developer, as well as by 130 human test subjects. Thirty of those test subjects interacted directly with the system and completed a survey of 13 questions/statements that asked the user about his/her experience using LCC. A second group of 100 human test subjects evaluated the dialogue logs of a subset of the first group of human testers. The collected results were all found to be acceptable and within the range of our expectations

Computers are buildings: on conceptual metaphors in the semantic field of computers and the Internet in Polish

The aim of the present paper is to discuss metaphorical constructions, based on fi gurative uses of words, in informal Polish in the fi eld of computers and the Inter-net. The study is based on the author’s own corpus, compiled on the basis of short informal texts (entries, posts) written on 32 selected Internet forums. Altogether, the corpus consists of 1,541,449 words. The paper, as the title suggests, focuses on one metaphorical formula, i.e. COMPUTERSAREBUILDINGS. The metaphors which can be subsumed under this heading belong to the most frequent in the corpus (along-side a different type, i.e. COMPUTERSAREHUMANS). They are discussed within the cognitive framework, as introduced by Lakoff and Johnson (1980). Some attention will also be devoted to the possible infl uence of English upon Polish metaphorical constructions used in the area of computers and the Internet

Questioning the Generational Divide: Technological Exoticism and Adult Constructions of Online Youth Identity

Part of the Volume on Youth, Identity, and Digital Media. This chapter reflects on the effects and implications of the discrepancy between adult perspectives on digital media and youth experiences. Through an analysis of public discourse by marketers, journalists, and new media researchers compared with statements by young technology users, it is proposed that the current so-called "Internet generation" is in fact a transitional generation, in which young Internet users are characterized to varying degrees by a dual consciousness of both their own and adult perspectives, the latter of which tend to exoticize youth. An analogy with the first television generation is developed to suggest that the birth of a true Internet generation, some years in the future, will pave the way for more normalized, difficult-to-question changes in media attitudes and consumption, and thus that the present transitional moment should be taken advantage of to encourage conversation between adults and youth about technology and social change

Enhancing oral fluency through task-fluency discussions in second life

107 Páginas.This qualitative exploratory action research was conducted with A2 university students at Universidad de la Sabana. They are immersed in blended learning practices where autonomy has become the key to succeed in learning processes. The needs analysis carried out in the target A2 population showed that there was a need to improve oral fluency and gain more motivation in virtual learning spaces. Therefore, the purpose of this study is to enhance oral fluency, which is understood as the ability to communicate by dealing with meaning rather than accuracy of language use. Thus, the approach that best responds to this aim is the implementation of Task-Fluency Discussions (Ur, 2012) which are effective tools to practice talking freely while learning from content and putting communicative strategies into action. Additionally, the present study proposes Second Life as a visual friendly virtual environment to foster communicative learning experiences. A process of participants´ self-reflection was followed by using a self-reflective-portfolio to review their progress in their oral performance, the further challenges and a potential action plan to achieve more fluent speaking. Those perceptions were also explored during in-depth group interviews. The teacher-researcher also compiled her own perceptions as individual, researcher and teacher in a reflective journal to supplement qualitative analysis. Findings indicate that Second Life is a tool that may empower oral participation and fluency, enhance autonomy and provide a more appealing virtual learning space. Oral fluency can be increased in several ways. Firstly, by accomplishing task-fluency discussions; secondly by making use of a self-access bank of words and expressions; by self-reflection upon achievements and future goals, all of which, in turn, may lead to new understanding of that being fluent means

Towards a framework for socially interactive robots

250 p.En las últimas décadas, la investigación en el campo de la robótica social ha crecido considerablemente. El desarrollo de diferentes tipos de robots y sus roles dentro de la sociedad se están expandiendo poco a poco. Los robots dotados de habilidades sociales pretenden ser utilizados para diferentes aplicaciones; por ejemplo, como profesores interactivos y asistentes educativos, para apoyar el manejo de la diabetes en niños, para ayudar a personas mayores con necesidades especiales, como actores interactivos en el teatro o incluso como asistentes en hoteles y centros comerciales.El equipo de investigación RSAIT ha estado trabajando en varias áreas de la robótica, en particular,en arquitecturas de control, exploración y navegación de robots, aprendizaje automático y visión por computador. El trabajo presentado en este trabajo de investigación tiene como objetivo añadir una nueva capa al desarrollo anterior, la capa de interacción humano-robot que se centra en las capacidades sociales que un robot debe mostrar al interactuar con personas, como expresar y percibir emociones, mostrar un alto nivel de diálogo, aprender modelos de otros agentes, establecer y mantener relaciones sociales, usar medios naturales de comunicación (mirada, gestos, etc.),mostrar personalidad y carácter distintivos y aprender competencias sociales.En esta tesis doctoral, tratamos de aportar nuestro grano de arena a las preguntas básicas que surgen cuando pensamos en robots sociales: (1) ¿Cómo nos comunicamos (u operamos) los humanos con los robots sociales?; y (2) ¿Cómo actúan los robots sociales con nosotros? En esa línea, el trabajo se ha desarrollado en dos fases: en la primera, nos hemos centrado en explorar desde un punto de vista práctico varias formas que los humanos utilizan para comunicarse con los robots de una maneranatural. En la segunda además, hemos investigado cómo los robots sociales deben actuar con el usuario.Con respecto a la primera fase, hemos desarrollado tres interfaces de usuario naturales que pretenden hacer que la interacción con los robots sociales sea más natural. Para probar tales interfaces se han desarrollado dos aplicaciones de diferente uso: robots guía y un sistema de controlde robot humanoides con fines de entretenimiento. Trabajar en esas aplicaciones nos ha permitido dotar a nuestros robots con algunas habilidades básicas, como la navegación, la comunicación entre robots y el reconocimiento de voz y las capacidades de comprensión.Por otro lado, en la segunda fase nos hemos centrado en la identificación y el desarrollo de los módulos básicos de comportamiento que este tipo de robots necesitan para ser socialmente creíbles y confiables mientras actúan como agentes sociales. Se ha desarrollado una arquitectura(framework) para robots socialmente interactivos que permite a los robots expresar diferentes tipos de emociones y mostrar un lenguaje corporal natural similar al humano según la tarea a realizar y lascondiciones ambientales.La validación de los diferentes estados de desarrollo de nuestros robots sociales se ha realizado mediante representaciones públicas. La exposición de nuestros robots al público en esas actuaciones se ha convertido en una herramienta esencial para medir cualitativamente la aceptación social de los prototipos que estamos desarrollando. De la misma manera que los robots necesitan un cuerpo físico para interactuar con el entorno y convertirse en inteligentes, los robots sociales necesitan participar socialmente en tareas reales para las que han sido desarrollados, para así poder mejorar su sociabilida

Using the MMORPG ‘RuneScape’ to Engage Korean EFL (English as a Foreign Language) Young Learners in Learning Vocabulary and Reading Skills

This study aims to explore the affordances offered by online role-playing games like RuneScape in learning English vocabulary and developing reading skills, and to examine whether there is any relationship between playing RuneScape and Korean children's vocabulary and reading skills. I sampled five elementary students (1 female and 4 males, aged 10-11), who played RuneScape for 30 minutes per session for 9 to 14 sessions in a private English institute in South Korea. I collected the text data through retrieving the text from the recordings of participants’ game-plays using a screen recorder. The observation data was attained by observing them playing games through participant observation, observation framework and field notes. I analysed the English text learners would encounter when playing Runescape, and using observation, attempted to describe the vocabulary and reading strategies they tend to use whilst playing. The findings showed that participants encountered the seven categories of vocabulary whilst playing: generally-used vocabulary, fixed phrases, RuneScape vernacular, lexis specific to computer games, chat speak (acronyms and abbreviations), emoticons and reduplication. From the observation data, I found that participants used the following vocabulary strategies: looking up in a dictionary, verbalising vocabulary and guessing meanings verbally. Reading strategies were: clicking, verbalising, reading texts aloud, translating and typing. The findings suggest that there is relationship between playing RuneScape and vocabulary and reading skills. However, Korean children do not get sufficient practice in their use of vocabulary and reading skills for pragmatic purposes in their English classrooms, due to time limitations and large classes. Children tend to lack instrumental motivation for learning English, so the fun and interest of playing games might help engage them in learning English. I would argue therefore that online role-playing games have the potential for Korean children as a useful supplementary tool for developing vocabulary and reading skills