Chatbot Theory: A naïve and elementary theory for dialogue management

Due to the increasing interested and use of chatbot, its properties and operation possibilities shall be proper realized matching both safety and security issues as well as present the several uses and compositions that this technology supports. This paper focus is on dialogue management since it is considered the core of a chatbot. The dialogue manager is responsible to, more than to transform an input sentence into an output one, hold the illusion of a human conversation. In this sense, it is presented an inceptive theoretical framework through a formal way for chatbots that can be used as a reference to explore, compose, build and discuss chatbots. The discussion is performed mostly on ELIZA since, due to its historical records, it can be considered an important reference chatbot, nevertheless, the proposed theory is compatible with the most recent technologies such those using machine and deep learning. The paper then presents some sketchy instances in order to explore the support provided by the theory.This paper has been supported by COMPETE: POCI-01-0145-FEDER-0070 43 and FCT – Fundação para a Ciência e Tecnologia - Project UID/CEC/ 00319/2013

한국인 고등학생의 영어 형용사 타동결과구문 학습에서의 인공지능 챗봇 기반 교수의 효과

학위논문(박사) -- 서울대학교대학원 : 사범대학 외국어교육과(영어전공), 2022.2. 김기택.English adjectival transitive resultative constructions (VtR) are notoriously challenging for Korean L2 English learners due to their syntactic and semantic differences from their L1 counterparts. To deal with such a complex structure, like English adjectival VtR, Korean L2 English learners need instructional interventions, including explicit instructions and corrective feedback on the target structure. Human instructors are virtually incapable of offering adequate corrective feedback, as providing corrective feedback from a human teacher to hundreds of students requires excessive time and effort. To deal with the practicality problems faced by human instructors in providing corrective feedback, numerous artificial intelligence (AI) chatbots have been developed to provide foreign language learners with corrective feedback on par with human teachers. Regrettably, many currently available AI chatbots remain underdeveloped. In addition, no prior research has been conducted to assess the effectiveness of corrective feedback offered by an AI chatbot, a human instructor, or additional explicit instruction via video material. The current study examined the instructional effects of corrective feedback from an AI chatbot on Korean high school students’ comprehension and production of adjectival VtR. Also, the current study investigated whether the corrective feedback generated by the AI chatbot enables Korean L2 English learners to expand their constructional repertoire beyond instructed adjectival VtR to uninstructed prepositional VtR. To investigate these issues, text-based Facebook Messenger AI chatbots were developed by the researcher. The effectiveness of the AI chatbots’ corrective feedback was compared with that of a human instructor and with additional video material. Students were divided into four groups: three instructional groups and one control group. The instructional groups included a chatbot group, a human group, and a video group. All learners in the three instructional groups watched a 5-minute explicit instruction video on the form and meaning pairings of the adjectival VtR in English. After that, learners were divided into three groups based on their preferences for instructional types. The learners volunteered to participate in the instructional procedures with corrective feedback from a text-based AI chatbot, a human instructor, or additional explicit instruction using a 15-minute video. Moreover, they took part in three testing sessions, which included a pretest, an immediate posttest, and a delayed posttest. The control group students were not instructed, and only participated in the three testing sessions. Two tasks were used for each test session: an acceptability judgment task (AJT) and an elicited writing task (EWT). The AJT tested participants’ comprehension of instructed adjectival VtR and uninstructed prepositional VtR. The EWT examined the correct production of instructed adjectival VtR and uninstructed prepositional VtR. The results of the AJT revealed that the instructional treatment (e.g., corrective feedback from the AI chatbot or a human instructor, or additional explicit instruction from the video material) was marginally more effective at improving the comprehension of adjectival VtR than was the case with the control group. On the other hand, the instructional treatment on the adjectival VtR failed in the generalization to prepositional VtR which was not overtly instructed. In the EWT, the participants in the corrective feedback groups (e.g., the chatbot and human groups) showed a more significant increase in the correct production of the instructed adjectival VtR more so than those in the video and control groups. Furthermore, the chatbot group learners showed significantly higher production of uninstructed prepositional VtR compared to any other group participants. These findings suggest that chatbot-based instruction can help Korean high school L2 English learners comprehend and produce complex linguistic structures—namely, adjectival and prepositional VtR. Moreover, the current study has major pedagogical implications for principled frameworks for implementing AI chatbot-based instruction in the context of foreign language learning.영어 형용사 타동결과구문(English Adjectival Transitive Resultative Construction)은 한국인 영어 학습자들에게 모국어의 대응 구문이 갖는 의미 통사론적 차이로 인해 학습하기 매우 어려운 것으로 알려져 있다. 따라서 영어 형용사 타동결과구문과 같은 복잡한 구문을 학습하기 위해서, 한국인 영어 학습자들에게는 목표 구조에 대한 명시적 교수와 교정적 피드백을 포함한 교수 처치가 요구된다. 수백 명의 학습자들에게 교정적 피드백을 제공하기 위해서는 과도한 시간과 노력이 요구되기 때문에, 인간 교사가 적절한 양의 교정적 피드백을 제공한다는 것은 사실상 불가능하다. 교정적 피드백을 제공할 때 직면하는 이러한 실용성 문제를 해결하기 위하여, 외국어 학습자들에게 인간 교사와 유사한 교정 피드백을 제공할 수 있는 수많은 인공 지능(AI) 챗봇이 개발되었다. 유감스럽게도, 현재 사용 가능한 많은 외국어 학습용 인공지능 챗봇은 아직 충분히 개발되지 않은 상태에 남아있으며, 인공지능 챗봇의 교정적 피드백이 갖는 교수효과를 비교 분석한 연구는 현재 이루어지지 않은 상태다. 이러한 선행연구의 한계에 초점을 두어, 본 연구에서는 인공지능 챗봇의 교정적 피드백이 한국 고등학생의 영어 형용사 타동결과구문의 이해와 생성에 미치는 교수 효과를 살펴보았다. 또한 본 연구에서는 이러한 교수 효과가 언어적으로 관련된 다른 영어 구문의 학습에도 영향을 끼치는지를 알아보기 위해 교실에서 직접 가르치지 않았던 구문인 영어 전치사 타동결과구문(English Prepositional Transitive Resultative Construction)의 학습 양상을 알아보았다. 이를 위해, 본 연구에서는 텍스트 메시지 기반의 페이스북 메신저에서 구동되는 인공지능 챗봇을 개발하였다. 인공지능 챗봇의 교수효과 검증을 위해 본 연구에 참여한 학생들은 네 개의 집단으로 구분되었다: 세 개의 교수 집단에는 교수처치가 적용되었고, 한 개의 통제 집단에서는 교수처치가 적용되지 않았다. 교수처치가 적용된 세 개의 집단은 챗봇그룹, 인간그룹, 영상그룹으로 분류되었으며, 이들은 모두 영어로 된 형용사 타동결과구문의 형태와 의미 쌍에 대한 5분 길이의 학습 비디오를 시청함으로써 명시적 교수 처치를 받았다. 또한 비디오를 시청한 후 세 그룹의 학습자들은 교재를 통해 제공되는 언어연습자료를 해결하는 과업에 참여하였다. 다음으로 세 집단(챗봇그룹, 인간그룹, 영상그룹)은 다음과 같은 추가적 교수처치를 받았다: 챗봇그룹 학습자들은 교재 활동과 관련된 텍스트 기반 인공지능 챗봇과의 대화에 참여함으로써 오류에 대한 교정적 피드백을 받았다. 인간그룹 학습자들은 교재활동을 완수한 내용을 인간 교사에게 전송하고, 이에 대한 교정적 피드백을 받았다. 영상그룹 학습자들은 교재활동을 완수한 후 이에 대한 15분의 추가적인 명시적 교수자료를 영상으로 시청하였다. 학습자의 교수효과는 사전시험, 사후시험 및 지연 사후시험으로 검증되었다. 한편 통제 집단 학생들은 교수처치 없이 세 번의 시험에만 참여하였다. 세 차례의 시험에서는 수용성판단과제(AJT)와 유도작문과제(EWT)의 두 가지 과제가 사용되었다. 수용성판단과제를 통하여, 교수된 영어 형용사 타동결과구문과 지시되지 않은 영어 전치사 타동결과구문 대한 참가자의 이해도를 측정하였다. 유도작문과제를 통하여 교수된 영어 형용사 타동결과구문과 지시되지 않은 영어 전치사 타동결과구문을 참여자가 정확하게 산출할 수 있는지를 측정하였다. 시험의 결과는 다음과 같았다. 수용성판단과제의 경우, 교수처치가 적용된 세 집단이 통제 집단보다 형용사 타동결과구문의 이해도 향상에 약간 더 효과적인 것으로 나타났다. 하지만 형용사 타동결과구문에 대한 교수적처치는 교수되지 않은 전치사 타동결과구문으로의 학습에 영향을 주지 못하였다. 유도작문과제의 경우, 인공지능 챗봇이나 인간 교사에 의해 제공되는 교정 피드백 그룹의 참가자가 영상그룹 및 통제집단의 참가자보다 형용사 타동결과구문의 올바른 생성에 더 유의미한 영향을 미치는 것으로 드러났다. 동일한 교수 효과가 전치사 타동결과구문의 학습에서도 관측되어, 형용사 타동결과구문의 학습이 전치사 타동결과구문의 학습에 일반화가 일어났다. 본 연구는 인간 교사가 직면해야 하는 실용성 문제를 극복하고, 인공지능 챗봇이 한국인 고등학교 L2 영어 학습자가 형용사 및 전치사 타동결과구문과 같은 복잡한 언어 구조를 이해하고 생성하는 데에 인간 교사와 비견될 정도로 교정적 피드백을 제공할 수 있을 것임을 시사한다. 또한, 본 연구는 인공지능 챗봇 기반 외국어 교육의 실제적 사례 및 효과를 선도적으로 보여주었다는 점에서 의미가 있다.ABSTRACT i TABLE OF CONTENTS iii LIST OF TABLES v LIST OF FIGURES vii CHAPTER 1. INTRODUCTION 1 1.1. Statement of Problems and Objectives 1 1.2. Scope of the Research 6 1.3. Research Questions 9 1.4. Organization of the Dissertation 10 CHAPTER 2. LITERATURE REVIEW 12 2.1. Syntactic and Semantic Analysis of Korean and English Transitive Resultative Constructions 13 2.1.1. Syntactic Analysis of English Transitive Resultative Construction 13 2.1.2. Syntactic Analysis of Korean Transitive Resultative Constructions 25 2.1.3. Semantic Differences in VtR between Korean and English 46 2.1.4. Previous acquisition study on English adjectival and prepositional VtR 54 2.2. Corrective Feedback 59 2.2.1. Definition of Corrective Feedback 59 2.2.2. Types of Corrective Feedback 61 2.2.3. Noticeability in Corrective Feedback 67 2.2.4. Corrective Recast as a Stepwise Corrective Feedback 69 2.3. The AI Chatbot in Foreign Language Learning 72 2.3.1. Non-communicative Intelligent Computer Assisted Language Learning (ICALL) 73 2.3.2. AI Chatbot without Corrective Feedback 79 2.3.3. AI Chatbot with Corrective Feedback 86 2.4. Summary of the Literature Review 92 CHAPTER 3. METHODOLOGY 98 3.1. Participants 98 3.2. Target Structure 102 3.3. Procedure of the Study 106 3.4. Instructional Material Shared by the Experimental Group 107 3.4.1. General Framework of the Instructional Session 108 3.4.2. Instructional Material Shared by Experimental Groups 111 3.5. Group-specific Instructional Treatments: Post-Written Instructional Material Activities on Corrective Feedback from Chatbot, Human, and Additional Explicit Instruction via Video 121 3.5.1. Corrective Feedback from the AI Chatbot 122 3.5.2. Corrective Feedback from a Human Instructor 136 3.5.3. Additional Instruction via Video Material 139 3.6. Test 142 3.6.1. Acceptability Judgment Task (AJT) 144 3.6.2. Elicited Writing Task (EWT) 150 3.7. Statistical Analysis 152 CHAPTER 4. RESULTS AND DISCUSSIONS 154 4.1. Results of Acceptability Judgment Task (AJT) 154 4.1.1. AJT Results of Instructed Adjectival VtR 155 4.1.2. AJT Results of Uninstructed Prepositional VtR 160 4.1.3. Discussion 164 4.2. Results of Elicited Writing Task (EWT) 175 4.2.1. EWT Results for Instructed Adjectival VtR 176 4.2.2. EWT Results of Uninstructed Prepositional VtR 181 4.2.3. Further Analysis 187 4.2.4. Discussion 199 CHAPTER 5. CONCLUSION 205 5.1. Summary of the Findings and Implications 205 5.2. Limitations and Suggestions for Future Research 213 REFERENCES 217 APPENDICES 246 ABSTRACT IN KOREAN 297박

Protectbot: A Chatbot to Protect Children on Gaming Platforms

Online gaming no longer has limited access, as it has become available to a high percentage of children in recent years. Consequently, children are exposed to multifaceted threats, such as cyberbullying, grooming, and sexting. The online gaming industry is taking concerted measures to create a safe environment for children to play and interact with, such efforts remain inadequate and fragmented. Different approaches utilizing machine learning (ML) techniques to detect child predatory behavior have been designed to provide potential detection and protection in this context. After analyzing the available AI tools and solutions it was observed that the available solutions are limited to the identification of predatory behavior in chat logs which is not enough to avert the multifaceted threats. In this thesis, we developed a chatbot Protectbot to interact with the suspect on the gaming platform. Protectbot leveraged the dialogue generative pre-trained transformer (DialoGPT) model which is based on Generative Pre-trained Transformer 2 (GPT-2). To analyze the suspect\u27s behavior, we developed a text classifier based on natural language processing that can classify the chats as predatory and non-predatory. The developed classifier is trained and tested on Pan 12 dataset. To convert the text into numerical vectors we utilized fastText. The best results are obtained by using non-linear SVM on sentence vectors obtained from fastText. We got a recall of 0.99 and an F_0.5-score of 0.99 which is better than the state-of-the-art methods. We also built a new dataset containing 71 predatory full chats retrieved from Perverted Justice. Using sentence vectors generated by fastText and KNN classifier, 66 chats out of 71 were correctly classified as predatory chats

Chatbot de Suporte para Plataforma de Marketing Multicanal

E-goi is an organization which provides automated multichannel marketing possibilities. Given its system’s complexity, it requires a not so smooth learning curve, which means that sometimes costumers incur upon some difficulties which directs them towards appropriate Costumer Support resources. With an increase in the number of users, these Costumer Support requests are somewhat frequent and demand an increase in availability in Costumer Support channels which become inundated with simple, easily-resolvable requests. The organization idealized the possibility of automating significant portion of costumer generated tickets with the possibility of scaling to deal with other types of operations. This thesis aims to present a long-term solution to that request with the development of a chatbot system, fully integrated with the existing enterprise modules and data sources. In order to accomplish this, prototypes using several Chatbot management and Natural Language Processing frameworks were developed. Afterwards, their advantages and disadvantages were pondered, followed by the implementation of its accompanying system and testing of developed software and Natural Language Processing results. Although the developed overarching system achieved its designed functionalities, the master’s thesis could not offer a viable solution for the problem at hand given that the available data could not provide an intent mining model usable in a real-world context.A E-goi é uma organização que disponibiliza soluções de marketing digital automatizadas e multicanal. Dada a complexidade do seu Sistema, que requer uma curva de aprendizagem não muito suave, o que significa que os seus utilizadores por vezes têm dificuldades que os levam a recorrer aos canais de Apoio ao Cliente. Com um aumento de utilizadores, estes pedidos de Apoio ao Cliente tornam-se frequentes e requerem um aumento da disponibilidade nos canais apropriados que ficam inundados de pedidos simples e de fácil resolução. A organização idealizou a possibilidade de automatizar uma porção significativa de tais pedidos, podendo escalar para outro tipo de operações. Este trabalho de mestrado visa apresentar uma proposta de solução a longo prazo para este problema. Pretende-se o desenvolvimento de um sistema de chatbots, completamente integrado com o sistema existente da empresa e variadas fontes de dados. Para este efeito, foram desenvolvidos protótipos de várias frameworks para gestão de chatbots e de Natural Language Processing, ponderadas as suas vantagens e desvantagens, implementado o sistema englobante e realizados planos de testes ao software desenvolvido e aos resultados de Natural Language Processing. Apesar do sistema desenvolvido ter cumprido as funcionalidades pelas quais foi concebido, a tese de mestrado não foi capaz de obter uma solução viável para o problema dado que com os dados disponibilizados não foi possível produzir um modelo de deteção de intenções usável num contexto real

‘IMPLICIT CREATION’ – NON-PROGRAMMER CONCEPTUAL MODELS FOR AUTHORING IN INTERACTIVE DIGITAL STORYTELLING

Interactive Digital Storytelling (IDS) constitutes a research field that emerged from several areas of art, creation and computer science. It inquires technologies and possible artefacts that allow ‘highly-interactive’ experiences of digital worlds with compelling stories. However, the situation for story creators approaching ‘highly-interactive’ storytelling is complex. There is a gap between the available technology, which requires programming and prior knowledge in Artificial Intelligence, and established models of storytelling, which are too linear to have the potential to be highly interactive. This thesis reports on research that lays the ground for bridging this gap, leading to novel creation philosophies in future work. A design research process has been pursued, which centred on the suggestion of conceptual models, explaining a) process structures of interdisciplinary development, b) interactive story structures including the user of the interactive story system, and c) the positioning of human authors within semi-automated creative processes. By means of ‘implicit creation’, storytelling and modelling of simulated worlds are reconciled. The conceptual models are informed by exhaustive literature review in established neighbouring disciplines. These are a) creative principles in different storytelling domains, such as screenwriting, video game writing, role playing and improvisational theatre, b) narratological studies of story grammars and structures, and c) principles of designing interactive systems, in the areas of basic HCI design and models, discourse analysis in conversational systems, as well as game- and simulation design. In a case study of artefact building, the initial models have been put into practice, evaluated and extended. These artefacts are a) a conceived authoring tool (‘Scenejo’) for the creation of digital conversational stories, and b) the development of a serious game (‘The Killer Phrase Game’) as an application development. The study demonstrates how starting out from linear storytelling, iterative steps of ‘implicit creation’ can lead to more variability and interactivity in the designed interactive story. In the concrete case, the steps included abstraction of dialogues into conditional actions, and creating a dynamic world model of the conversation. This process and artefact can be used as a model illustrating non-programmer approaches to ‘implicit creation’ in a learning process. Research demonstrates that the field of Interactive Digital Storytelling still has to be further advanced until general creative principles can be fully established, which is a long-term endeavour, dependent upon environmental factors. It also requires further technological developments. The gap is not yet closed, but it can be better explained. The research results build groundwork for education of prospective authors. Concluding the thesis, IDS-specific creative principles have been proposed for evaluation in future work

A Comparative Analysis of NLP Algorithms for Implementing AI Conversational Assistants

The rapid adoption of low-code/no-code software systems has reshaped the landscape of software development, but it also brings challenges in usability and accessibility, particularly for those unfamiliar with the specific components and templates of these platforms. This thesis targets improving the developer experience in Nokia Corporation's low-code/no-code software system for network management through the incorporation of Natural Language Interfaces (NLIs) using Natural Language Processing (NLP) algorithms. Focused on key NLP tasks like entity extraction and intent classification, we analyzed a variety of algorithms, including MaxEnt Classifier with NLTK, Spacy, Conditional Random Fields with Stanford NER for entity recognition, and SVM Classifier, Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, and RASA DIET for intent classification. Each algorithm's performance was rigorously evaluated using a dataset generated from network-related utterances. The evaluation metrics included not only performance metrics but also system metrics. Our research uncovers significant trade-offs in algorithmic selection, elucidating the balance between computational cost and predictive accuracy. It reveals that while some models, like RASA DIET, excel in accuracy, they require extensive computational resources, making them less suitable for lightweight systems. In contrast, simpler models like Spacy and StanfordNER provide a balanced performance but require careful consideration for specific entity types. While the study is limited by dataset size and focuses on simpler algorithms, it offers an empirically grounded framework for practitioners and decision-makers at Nokia and similar corporations. The findings point towards future research directions, including the exploration of ensemble methods, the fine-tuning of existing models, and the real-world implementation and scalability of these algorithms in low-code/no-code platforms

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Language Processing and the Artificial Mind: Teaching Code Literacy in the Humanities

Humanities majors often find themselves in jobs where they either manage programmers or work with them in close collaboration. These interactions often pose difficulties because specialists in literature, history, philosophy, and so on are not usually code literate. They do not understand what tasks computers are best suited to, or how programmers solve problems. Learning code literacy would be a great benefit to humanities majors, but the traditional computer science curriculum is heavily math oriented, and students outside of science and technology majors are often math averse. Yet they are often interested in language, linguistics, and science fiction. This thesis is a case study to explore whether computational linguistics and artificial intelligence provide a suitable setting for teaching basic code literacy. I researched, designed, and taught a course called “Language Processing and the Artificial Mind.” Instead of math, it focuses on language processing, artificial intelligence, and the formidable challenges that programmers face when trying to create machines that understand natural language. This thesis is a detailed description of the material, how the material was chosen, and the outcome for student learning. Student performance on exams indicates that students learned code literacy basics and important linguistics issues in natural language processing. An exit survey indicates that students found the course to be valuable, though a minority reacted negatively to the material on programming. Future studies should explore teaching code literacy with less programming and new ways to make coding more interesting to the target audience

Human-Computer Interaction

In this book the reader will find a collection of 31 papers presenting different facets of Human Computer Interaction, the result of research projects and experiments as well as new approaches to design user interfaces. The book is organized according to the following main topics in a sequential order: new interaction paradigms, multimodality, usability studies on several interaction mechanisms, human factors, universal design and development methodologies and tools