## Linear Regression & Chatbots

Notes:

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It is a widely used technique in many different fields, including economics, finance, psychology, and biology.

In the context of chatbots, linear regression can be used to model the relationship between user input and the chatbot’s response. For example, a chatbot may use linear regression to predict the user’s next question, based on their previous questions and the chatbot’s previous responses.

To use linear regression in a chatbot, the chatbot first needs to collect a large amount of data about user input and chatbot responses. This data can be used to train a linear regression model, which can then be used to make predictions about future user input.

To make a prediction using linear regression, the chatbot first needs to input the user’s previous questions and the chatbot’s previous responses into the model. The model will then use this information to predict the user’s next question. The chatbot can then use this prediction to generate an appropriate response.

Overall, linear regression is a powerful tool that can be used to improve the performance of chatbots. It allows chatbots to make more accurate predictions about user input, and to generate more relevant and personalized responses. This can help to improve the user experience and make chatbots more effective and engaging.

A Large-Scale User Study of an Alexa Prize Chatbot: Effect of TTS Dynamism on Perceived Quality of Social Dialog
M Cohn, CY Chen, Z Yu – Proceedings of the 20th Annual SIGdial …, 2019 – aclweb.org
… impact of adding interjections and fillers in the Alexa TTS voice in our chatbot (Chen et … We hypothesize that in a social dialog system, adding interjections (eg, “Awesome!”) and filler words (eg, “um … interaction on a scale from 1-to-5) with a mixed effects linear regression with the …

A framework for building closed-domain chat dialogue systems
M Nakano, K Komatani – Knowledge-Based Systems, 2020 – Elsevier
… Closed-domain chatbot. Dialogue system development framework. Non-task-oriented dialogue system … Section 2 mentions previous work related to closed-domain chatbots … It won first prize at the dialogue system live competition held in Nov., 2018 [9]. The system built by …

The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning
N Shi, Q Zeng, R Lee – arXiv preprint arXiv:2009.13984, 2020 – arxiv.org
… The above chatbots discussed are basically adopting the Seq2Seq model, it gets good performance for … Transfer Transfo we used as chatbot in our agent is a language system … second approach is interpretable models such as casual models like linear regression, Bayes, logistic …

Dialogue systems for language learning: a meta-analysis
S Bibauw, T François… – Language Learning & …, 2020 – serge.bibauw.be
… Recently, with the increased prevalence of chatbots and digital personal assistants, a … for language learning purposes: intelligent tutoring systems, conversational agents, dialogue systems, chat- bots, etc … tional turns—with any sort of automated agent (chat- bot, robot, embodied …

Survey on evaluation methods for dialogue systems
J Deriu, A Rodrigo, A Otegi, G Echegoyen… – Artificial Intelligence …, 2020 – Springer

Approximating interactive human evaluation with self-play for open-domain dialog systems
A Ghandeharioun, JH Shen, N Jaques… – Advances in Neural …, 2019 – papers.nips.cc
… Hybrid metric (MH) We combine the aforementioned metrics (Mi) using linear regression, and optimize their … evaluation is costly, we propose a self-play scenario where the dialog system talks to … be calculated on the trajectory of self-play utterances for any chatbot, regardless of …

CBET: design and evaluation of a domain-specific chatbot for mobile learning
Q Liu, J Huang, L Wu, K Zhu, S Ba – Universal Access in the Information …, 2019 – Springer
… To enhance the chatbots applied in mobile learning, we pro- pose an intelligent chatbot for the field of educational tech- nology (CBET). This chatbot features a question-and-answer service in a specific domain … 1 Conceptual architecture of our proposed domain-specific chat- bot …

Chatbot advertising effectiveness: When does the message get through?
E Van den Broeck, B Zarouali, K Poels – Computers in Human Behavior, 2019 – Elsevier
… First, a multiple linear regression was conducted with perceived intrusiveness of chatbot advertising … helpfulness of the chatbot significantly predicted perceived intrusiveness of chatbot advertising (? … When chatbots were perceived as helpful and useful, intrusiveness of chatbot …

Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems
V Phy, Y Zhao, A Aizawa – arXiv preprint arXiv:2011.00483, 2020 – arxiv.org
… For example, specificity is preferred in food-ordering chatbots, whereas fluency is preferred in language-teaching chatbots … Eskenazi (2020), denoted as USLS-A. We utilize the weights obtained from the linear regression (Figure 2 … Towards a human-like open-domain chatbot …

A survey on construction and enhancement methods in service chatbots design
Z Peng, X Ma – CCF Transactions on Pervasive Computing and …, 2019 – Springer
… 2, modular task-oriented dialog system mainly consists of three components (Shum et al … Microsoft LUIS (2018), IBM Watson Assistant (2018) and Dialogflow (2018) that provide easy-to-use SLU, DM and NLG services to help chatbot designers build service chatbots …

Open Domain Chatbot Based on Attentive End-to-End Seq2Seq Mechanism
SS Abdullahi, S Yiming, A Abdullahi… – Proceedings of the 2019 …, 2019 – dl.acm.org
… Simple Machine Learning techniques like Support Vector Machines (SVM), linear regression and nai?ve Bayes methods learn the correlation … We therefore suggest that more data networked together can give the chatbot multi-domain intelligence to … Deep Learning for ChatBots …

On the usefulness of the preliminary Usability Satisfaction Questionnaire (USQ), its dimensionality, and the impact of user characteristics
A Dehmel – 2020 – essay.utwente.nl
… 19 3.3 Linear regression of demographic characteristics … Chatbot tasks ….. 109 … Chatbots are technical dialogue systems which are capable of communicating with a user despite …

What Matters for Chatbots? Analyzing Quality Measures for Facebook Messenger’s 100 Most Popular Chatbots
J Pereira, Ó Díaz – Towards Integrated Web, Mobile, and IoT Technology, 2019 – Springer
… Using R, we obtain a linear regression model with all the factors of Table 2 … smarterwithgartner/ chatbots-will-appeal-to-modern-workers/. 5. Klüwer, T.: From chatbots to dialog … 1–22 (2011)Google Scholar. 6. Kuligowska, K.: Commercial chatbot: performance evaluation, usability …

Predicting ratings of real dialogue participants from artificial data and ratings of human dialogue observers
K Georgila, C Gordon, V Yanov, D Traum – Proceedings of The 12th …, 2020 – aclweb.org
… Using linear regression, we developed di- alogue evaluation functions based on features from the sim … next utterance classi- fication was introduced as a method for evaluating chatbots (Lowe et … Also, topic-based metrics for chatbot evaluation (topic breadth and topic depth) were …

Personalized reason generation for explainable song recommendation
G Zhao, H Fu, R Song, T Sakai, Z Chen, X Xie… – ACM Transactions on …, 2019 – dl.acm.org
… in the availability of con- versational data has enabled rapid development of chatbots and dialog … some of these comments are too long or too detailed for a chatbot and should … Thus, we propose learning a linear regression function of a score based on generation prob- ability, a …

Delivering Cognitive Behavioral Therapy Using A Conversational SocialRobot
F Dino, R Zandie, H Abdollahi, S Schoeder… – arXiv preprint arXiv …, 2019 – arxiv.org
… recognition and natural language processing techniques has allowed for chatbots and dialogue … Another experiment created a chatbot with emotional capabilities, however for sentence … Fig. 5. Scaled sentiment values (positive and negative) and their linear regression for four …

Survey on evaluation methods for dialogue
JM Deriu, A Rodrigo, A Otegi, E Guillermo, S Rosset… – 2019 – digitalcollection.zhaw.ch
… There are many different approaches to design a dialogue manager, which are partly dictated by the application of the dialogue system. However, there are three broad classes of dialogue systems, which we encounter in the literature: task-oriented systems, conversational …

Development and Validation of a Corpus for Machine Humor Comprehension
YH Tseng, WS Wu, CY Chang, HC Chen… – Proceedings of The 12th …, 2020 – aclweb.org
… As human-dialogue systems, chatbots, or conversational user interface become versatile in this … validated the labels by developing a retrieval- based chatbot, called IceBreaker … Validation We have applied three regression techniques, namely Linear Regression, Linear Support …

An assessment framework for dialport
K Lee, T Zhao, S Ultes, L Rojas-Barahona… – … Social Interaction with …, 2019 – Springer
… The original PARADISE framework learns a linear regression [7] to weight the importance of each cost and success in order to predict the users’ subjective scores … the Portal agent should select the chatbot … In: Workshop on Chatbots and conversational agentsGoogle Scholar …

Effects of interactivity of written practice on incidental vocabulary acquisition
S Bibauw, T François, P Desmet – dial.uclouvain.be
… with an automated agent [ chatbot, talking robot, automated personal assistant, conversational agent, non- player character in a video game… ] … 0.75 1.00 Dialogue System Dialogue Completion Control Score … 31 Results Linear regression with mixed effects modelling ? SE F df p …

User Impressions of Questions to Acquire Lexical Knowledge
K Komatani, M Nakano – Proceedings of the 21th Annual Meeting of the …, 2020 – aclweb.org
… Pappu and Rud- nicky (2014) designed strategies for asking users questions in a goal-oriented dialogue system and analyzed the acquired knowledge through a user study. Hixon et al … We ex- plained that they would talk with an “AI chatbot” … 4 Analysis with Linear Regression …

LSTM for Dialogue Breakdown Detection: Exploration of Different Model Types and Word Embeddings
M Hendriksen, A Leeuwenberg… – … Workshop on Chatbots …, 2019 – workshop.colips.org
… The re- gression method implied the application of linear regression with the probability … stance, [22] offer a similar technique for assessment of chatbot responses in … Overview of dialogue breakdown detection challenge 3. Proceedings of Dialog System Technology Challenge 6 …

BigBlueBot: teaching strategies for successful human-agent interactions
JD Weisz, M Jain, NN Joshi, J Johnson… – Proceedings of the 24th …, 2019 – dl.acm.org
… Participants also felt the chatbots were helpful to them in accomplishing their tasks … We created two linear regression models to understand which behavioral measures accounted for empathy … These models included age, gender, which chatbot was experienced first, the amount …

A framework to incorporate aspects of social perception in synthetic voices
SS Rallabandi – researchgate.net
… the rapidly growing interest in human-machine inter- actions and spoken dialog systems, Chatbots have become in … speech community is fo- cusing on personalization of these chatbots for various … Further, I employed backward selection in linear regression, for feature selection …

Double-Linear Thompson Sampling for Context-Attentive Bandits
D Bouneffouf, R Féraud, S Upadhyay… – arXiv preprint arXiv …, 2020 – arxiv.org
… In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, motivated by various practical applications, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of …

Toward low-cost automated evaluation metrics for Internet of Things dialogues
K Georgila, C Gordon, H Choi, J Boberg, H Jeon… – … Dialogue System …, 2019 – Springer
… next utterance classification was introduced as a method for evaluating chatbots [14], but the … Recently, topic-based metrics for chatbot evaluation (topic breadth and topic depth) were … We applied linear regression to the training set, calculated our evaluation functions, and then …

Development of customized conversational interfaces with Deep Learning techniques
P Cañas Castellanos – 2020 – e-archivo.uc3m.es
… project is to show the whole process of the development of a spoken dialog system with Deep … However, it has been shown that using a chatbot for such functionality can multiply your … be used for classification tasks, while ADALINE can also be used for linear regression problems …

Zipf’s Law in Human-Machine Dialog
GM Linders, MM Louwerse – Proceedings of the 20th ACM International …, 2020 – dl.acm.org
… The WOCHAT corpus includes dialogs between users and chat- bots [12] … MLE has been shown to give better fits than its alternative, a linear regression on a log-log plot … The most likely explanation is that many chatbots reuse words or phrases from the user in their response …

Reinforcement learning for Dialogue Systems optimization with user adaptation.
N Carrara – 2019 – tel.archives-ouvertes.fr
… The first proposed approach involves clustering of Dialogue Systems (tailored for their respective user) based on their behaviours … The second idea states that before using a dedicated Dialogue System, the first in- teractions with a user should be handled carefully by a safe …

Intelligent Forecasting System for NPP’s Energy Production
O Chornovol, G Kondratenko, I Sidenko… – 2020 IEEE Third …, 2020 – ieeexplore.ieee.org
… models used in the task of forecasting energy production: Multiple Linear Regression (MLR), Support … human machine interaction for synthesis of the intelligent dialogue chatbot,” 10th IEEE … [27] P. Kushneryk, Y. Kondratenko, and I. Sidenko, “Intelligent dialogue system based on …

Latest Developments in Deep Learning in Finance 8th November 2019
NYU Courant – 2019 – pdfs.semanticscholar.org
… Factor Model Results Linear Regression FFWD Neural Network Page 25. 25 … Machine translation • Spoken dialog systems • Complex question answering NLP in Industry … Speech recognition • Chatbots / Dialog agents • Automating customer support • Controlling devices …

An open-source dialog system with real-time engagement tracking for job interview training applications
Z Yu, V Ramanarayanan, P Lange… – … Social Interaction with …, 2019 – Springer
… [19], a chatbot reacts to … System architecture of the HALEF dialog system that incorporates an engagement tracking module … The simple linear regression analysis performed a least-squares optimization of the following cost function: \begin{aligned} min_{\alpha \beta } \sum ^{n …

Carecall: a call-based active monitoring dialog agent for managing covid-19 pandemic
SW Lee, H Jung, SH Ko, S Kim, H Kim, K Doh… – arXiv preprint arXiv …, 2020 – arxiv.org
… Firstly, a voice-based dialog system is required to be able to under- stand unexpected type of user utterances … To evaluate the individual infection, a previous work uses linear regression with the person’s symptoms as a feature … Towards a human-like open-domain chatbot …

Efficacy of Deep Neural Embeddings based Semantic Similarity in Automatic Essay Evaluation
M Hendre, P Mukherjee, R Preet… – International Journal of …, 2020 – journal.uob.edu.bh
… In tasks like sentiment Analysis, Chatbot, Question Answering, Automatic Essay Evaluation, Dialogue Systems, Parsing, Word … Essay specific 11 features along with the Bayesian and K-nearest neighbor classifier scores are combined using linear regression to predict …

Entrainment in social robots: the influence of prosodic entrainment by second language tutor robots on student engagement
AA Polimeno – 2020 – dspace.library.uu.nl
… ucational tools such as chat bots can be especially helpful for children who are … found that the amount of entrainment by students to the dialogue system was positively … and duration values, preprocessing the data, and statistical analysis utilizing mixed linear regression modelling …

An Overview of the Application of Artificial Intelligence in Education
M Hashimu, NA Anka – pdfs.semanticscholar.org
… These sub-sections are (i) automated grader, (ii) Adaptive learning, (iii) chatbot, and (iv) virtual facilitator … In addition, linear regression techniques is mostly used for model training along with ensuring the use of different other classification and put clothing … Figure 2.4 Chatbots …

Beyond turing: Intelligent agents centered on the user
M Eskenazi, S Mehri, E Razumovskaia… – arXiv preprint arXiv …, 2019 – arxiv.org
… Liu et al (2016) review dialog system assessment approaches … Real users will not use a chatbot that re- peats the same thing several times, even if it was … DialPort is a hybrid system including both slot-filling sys- tems (Cambridge) and chatbots (UCSC’s Slugbot and CMUs Qubot …

Estimation Method of L2 Learners’ Second Language Ability by using Features in Conversation
X Chen, MHURR Khan, K Wakabayashi – Proceedings of the 21st …, 2019 – dl.acm.org
… The use of chatbots has notably increased recently in many fields especially education and artificial intelligence make it more promising … The goal of Unriza and Carolina’s master thesis [18] was to make a chatbot with user having a natural conversation which … Linear Regression …

Rehabilitace paže pomocí detekce pohybu ve videu
M Ryba – 2020 – dspace5.zcu.cz
… Various proposed solutions to the problem, such as usage of Virtual Reality (VR) (§2.5.1) or dialogue systems (§2.5.2), are thus … Dialogue system is a special type of software designed to interact with its users by simulating human conversation through text or synthesised voice …

A comparative study of social bot classification techniques
F Örnbratt, J Isaksson, M Willing – 2019 – diva-portal.org
… Web Robots (crawlers) ? Chatbots (natural language based dialog system) ? Spambots (bots that advertise and post spam on online messaging platforms) … linear regression, logistic regression, naive bayes, k-nearest-neighbour, decision tree and random …

Build it break it fix it for dialogue safety: Robustness from adversarial human attack
E Dinan, S Humeau, B Chintagunta… – arXiv preprint arXiv …, 2019 – arxiv.org
… public fo- rums (Galán-Garc?a et al., 2016), and the de- ployment of chatbots in the … Adversarial attacks on the Tay chatbot led to the developers shutting down the system (Wolf et … Many approaches have been taken to solve these tasks – from linear regression and SVMs to deep …

Building A User-Centric and Content-Driven Socialbot
H Fang – arXiv preprint arXiv:2005.02623, 2020 – arxiv.org
… bots in the literature have adopted approaches different from task-oriented systems including … based chatbots, including speech recognition errors, lack of sentence segmentation, disfluencies … two popular approaches used for chatbot systems, ie, scripting responses with template …

Enabling IoT in Education 4.0 with BioSensors from Wearables and Artificial Intelligence
MI Ciolacu, L Binder, H Popp – 2019 IEEE 25th International …, 2019 – ieeexplore.ieee.org
… The chatbot for mathematics answers the questions with voice output or in writing feedback … have been used, based on the following methods: cosine similarity, ontology (A03O), latent semantic analysis, IBM Watson’s Discovery Service (A06W), linear regression, support vector …

Usability evaluation of spoken humanoid embodied conversational agents in mobile serious games
D Korre – 2019 – era.ed.ac.uk
… ii Page 6. iii Abstract The use of embodied conversational agents (ECAs) and spoken dialogue systems in … and emotions. Despite these theoretical advantages, according to recent studies, the interaction with spoken dialogue systems, either in the form of an embodied agent …

Crowd of Oz: a crowd-powered social robotics system for stress management
T Abbas, VJ Khan, U Gadiraju, E Barakova… – Sensors, 2020 – mdpi.com
… Chorus [41] is a text-based chatbot that assists end-users with information retrieval tasks by conversing with online synchronous group of … on Chorus by employing both machine learning and human computation to enable a group of crowd-workers to collaborate with chatbots …

Chatbot learning partners: Connecting learning experiences, interest and competence
LK Fryer, K Nakao, A Thompson – Computers in Human Behavior, 2019 – Elsevier
… Attesting to the potential of this early chatbot—and the many chatbots that have and continue to follow—is the fact that despite the early chatbot’s weaknesses (limited to very narrow ranges of questions), users have reported preferring to discuss their feelings with machines …

Simulating the Effects of Social Presence on Trust, Privacy Concerns & Usage Intentions in Automated Bots for Finance
M Ng, KPL Coopamootoo, E Toreini… – 2020 IEEE European …, 2020 – ieeexplore.ieee.org
… We computed a linear regression with predictors social presence, privacy concern and trust in bot (and the depen- dent variable being intention to use chatbot) to investigate H4 … concern do not have a significant impact on intention to use the imagined chatbots.” Our data met …

Keeping Up Appearances: Computational Modeling of Face Acts in Persuasion Oriented Discussions
R Dutt, R Joshi, CP Rosé – arXiv preprint arXiv:2009.10815, 2020 – arxiv.org
… To quantify the impact, we perform linear regression with the donation probability at each time step (yi) as the dependent variable. The independent variables in- cludes the predicted face acts for that step (fk i ) and the donation probability at the previous step yi-1 …

Diving Deep into Deep Learning: History, Evolution, Types and Applications
HCA Deekshith Shetty, MJ Varma, S Navi, MR Ahmed – researchgate.net
… before. TABLE I MACHINE LEARNING ALGORITHMS Supervised Learning Regression Classification Simple Linear Regression Logistic Regression Multiple Linear Regression K-Nearest Neighbour (KNN) Polynomial Regression …

Socially-Aware Dialogue System
R Zhao – 2019 – lti.cs.cmu.edu
… However, social chatbots fall short in replicating the interpersonal function of communication … SAPA) This chapter reviews our knowledge-inspired socially-aware dialogue system in a … recognition of conversational strategies in the service of a socially-aware dialog system …

What Is Artificial Intelligence and How to Exploit It?
RT Kreutzer, M Sirrenberg – Understanding Artificial Intelligence, 2020 – Springer
… The output variables must also be defined. The algorithm is trained on the entered data to find the connection between the input variables and the output variables. The methods used include linear regression, linear discriminant analysis and the decision tree method …

The impact of chatbot conversational skill on engagement and perceived humanness
RM Schuetzler, GM Grimes… – Journal of Management …, 2020 – Taylor & Francis
… CAs are often operationalized as chatbots which are used for many applications including technical support, customer service, and … Social Presence Theory to describe how conversational skill influences perceived social presence and ultimately anthropomorphism of a chatbot …

Affective computing and crowdsourcing: subjective labels and sequential effects
JH Shen – 2019 – dspace.mit.edu
… 67 6.1 Summary of the accuracy and mean squared error (MSE) from the linear regression model compared to a constant prediction baseline on … Currently, human evaluation of dialog systems involves reading a conversation … recognition and dialog system evaluation …

Making Pepper Understand and Respond in Romanian
D Tufis, VB Mititelu, E Irimia, M Mitrofan… – … on Control Systems …, 2019 – ieeexplore.ieee.org
… based) and this feature differentiates them from the dialogs carried on chatbots … better decorrelation 6. Applying fMLLR (feature space Maximum Likelihood Linear Regression) to normalize … H. Thompson, T. Winograd, “GUS, A Frame-Driven Dialog System,” Artificial Intelligence …

How digitalization affects insurance companies: overview and use cases of digital technologies
C Eckert, K Osterrieder – Zeitschrift für die gesamte …, 2020 – Springer
The digital transformation is of increasing relevance for insurance companies’ business models. It leads to opportunities as well as challenges, espe.

Bandit Algorithms to Personalize Educational Chatbots
W Cai, J Grossman, ZJ Lin, H Sheng, JTZ Wei… – 2020 – itsmrlin.com
… 3 Page 4. Chatbots. Chatbots have been widely applied to various domains, such as customer service [47], college management [7], and purchase recommendation [22]. One approach to building a chatbot is to construct rule-based input-to-output mappings [2, 48] …

A smartphone-based health care chatbot to promote self-management of chronic pain (SELMA): pilot randomized controlled trial
S Hauser-Ulrich, H Künzli… – JMIR mHealth and …, 2020 – mhealth.jmir.org
… Each message sequence begins with a warm greeting, in which the chatbot enquires about the participant’s mood and replies in an … We also assessed the relationship between the adherence ratios and study outcomes by linear regression analysis for the intervention group …

Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry
N Köbis, LD Mossink – Computers in Human Behavior, 2020 – Elsevier
… In 2014, a chat bot, called Eugene Goostman, was claimed to have passed the Turing Test, by tricking 33% of human judges into believing they were communicating with a 13-year-old Ukrainian boy (Marcus, Rossi, & Veloso, 2016; Walsh, 2017; Warwick & Shah, 2016) …

Ambient Assisted Living with Deep Learning
E Merdivan – 2019 – tel.archives-ouvertes.fr
… important components: improving activity recognition, addressing privacy concerns and developing intelligent dialogue systems for AAL systems, with an emphasis on a framework which is flexible and scalable for real-world applications. Page 20. Chapter 1. Introduction 3 …

Statistical natural language processing methods for intelligent process automation
A Moiseeva – 2020 – edoc.ub.uni-muenchen.de
… Wie bereits erwähnt, sind Chat- bots eine der zentralen Anwendungen für die IPA-Domäne, da … applications of NLP within the IPA domain – are conversational interfaces (eg, chatbots) that are … In a conventional chat- bot system, a user provides input in a natural language, the …

Understanding Artificial Intelligence
RT Kreutzer, M Sirrenberg – 2020 – Springer
Page 1. Management for Professionals Understanding Artificial Intelligence Ralf T. Kreutzer · Marie Sirrenberg Fundamentals, Use Cases and Methods for a Corporate AI Journey Page 2. Management for Professionals Page 3 …

The Influencing Factors of Trust in Human-like Agents
??? – 2020 – s-space.snu.ac.kr
Page 1. ?????-???-???? 2.0 ???? ???? ??? ??? ??? ??? ??? ???? ? ? ???? ??, ??, ??, ??, ?? ? ??? ? ????. ??? ?? ??? ??? ???: ? ???, ? ???? …

Follow-up Question Generation
Y Mandasari – 2019 – essay.utwente.nl
… Another example is virtual assistants in an online shopping website, where a task-oriented dialogue system … Generally, chatbots carry an en- tertainment aspect … Chatbot architectures are generally distinguished into two classes: rule-based systems and corpus-based systems …

Communication formats and their impact on patient perception and working mechanisms: A mixed-methods study of chat-based vs. face-to-face psychotherapy for …
A Gieselmann, C Podleschka, A Rozental… – Behavior Therapy, 2020 – Elsevier
… chat; media richness theory; chatbot; insomnia Journal Pre-proof Page 5. Journal Pre-proof … interactions with their Internet-based therapist or even interactions with non-human chatbots … Results of the further session evaluations were analyzed using multilevel linear regression …

Deep learning for nlp and speech recognition
U Kamath, J Liu, J Whitaker – 2019 – Springer
Page 1. Uday Kamath · John Liu · James Whitaker Deep Learning for NLP and Speech Recognition Page 2. Deep Learning for NLP and Speech Recognition Page 3. Uday Kamath • John Liu • James Whitaker Deep Learning for NLP and Speech Recognition 123 Page 4 …

Creative Artificial Intelligence–Algorithms vs. humans in an incentivized writing competition
N Köbis, L Mossink – arXiv preprint arXiv:2005.09980, 2020 – arxiv.org
… In 2014, a chat bot, called Eugene Goostman, was claimed to have passed the Turing Test, by tricking 33% of Page 5 … On the other end of the spectrum are unfiltered algorithmic outputs, such as many chatbots, tweetbots and other automated text-generating algorithms …

Comparison and efficacy of synergistic intelligent tutoring systems with human physiological response
F Alqahtani, N Ramzan – Sensors, 2019 – mdpi.com
The analysis of physiological signals is ubiquitous in health and medical diagnosis as a primary tool for investigation and inquiry. Physiological signals are now being widely used for psychological and social fields. They have found promising application in the field of computer …

Novel Methods for Efficient Dialogue Policy Learning by Improving Agent-User Interaction
B Peng – 2019 – search.proquest.com
… Research and Development of Human Assist AI to Build Chatbot (Journal Paper, work in progress, proposal funded by ITF) ix Page 12 … realize this goal. A spoken dialogue system is a computational … speech, text. Spoken dialogue systems have long been of inter …

Exploring Interaction with Remote Autonomous Systems using Conversational Agents
DA Robb, J Lopes, S Padilla, A Laskov… – Proceedings of the …, 2019 – dl.acm.org
… TRIPS is a combination between a CA (also known as dialog system) and “Specialized … Evaluation of non-task oriented social dialog systems, on the other hand, is a new … to capture the multi-dimensionality and complexity of dialog through multi-linear regression analysis (MLR …

Designing a Sustainable Virtual Assistant
CH Low – 2019 – ntnuopen.ntnu.no

Assessing the Impact of Cognitive Assistants on Mental Workload in Simple Tasks
T Maier, V Donghia, C Chen… – … and Information in …, 2019 – asmedigitalcollection.asme.org
… established requirements for spoken dialogue systems and applied those requirements to a CA … However, a linear regression analysis using the number of suggestions (independent variable) and the … beyond the one in this study should also be considered (eg chat bots) …

An initial model of trust in chatbots for customer service—Findings from a questionnaire study
CB Nordheim, A Følstad, CA Bjørkli – Interacting with Computers, 2019 – academic.oup.com
… The contribution of our study is a model that includes three groups of factors assumed to be relevant for users’ trust in chatbots for customer service: chatbot-related factors such as perceived expertise and responsiveness, environment-related factors such as risk and brand …

Trust in Consumer Adoption of Artificial Intelligence-Driven Virtual Finance Assistants: A Technology Acceptance Model Perspective
DB Nashold Jr – 2020 – search.proquest.com
… moderated linear regression. By examining these relationships, the research here has … understanding of how trust influences consumers’ intentions to use / adopt chatbot Page 16. 5 services … driven chatbots. Consumers’ trust in the competence of a financial institution in general …

DEEP NEURAL NETWORK MODELS FOR SEQUENCE LABELING AND COREFERENCE TASKS
BM Sergeevich – mipt.ru
… 86 3.9 Annotating step of a voice-enabled chatbot. . . . . 88 … Some impor- tant supervised learning algorithms include k-Nearest Neighbors, Linear Regression, Logistic … 1966 ELIZA ELIZA was one of the earliest chatbots, developed by Joseph …

A data-efficient deep learning approach for deployable multimodal social robots
H Cuayáhuitl – Neurocomputing, 2020 – Elsevier
… 11], where the Nico humanoid torso robot plays the game of rock-paper-scissors using a ‘Wizzard of Oz’ setting; [12], where the Sky humanoid robot plays catch and juggling using inverse kinematics and induced parameters with least squares linear regression; [13], where the …

Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents
D Park, H Yuan, D Kim, Y Zhang, M Spyros… – arXiv preprint arXiv …, 2020 – arxiv.org
… In the area of spoken dialogue system, PARADISE (Walker et al., 1997) proposed a framework for … dialogue, specifying the relative contribution of various factors via a linear regression model … can be extended to other conversational system and text-based chatbot applications …

Modelling speaker adaptation in second language learner dialogue
AJ Sinclair – 2020 – era.ed.ac.uk
… While this adapta- tion is natural to humans, it is an open problem for dialogue systems, where managing … An example of this constrained style of interaction was Duolingo’s chat-bot, which allowed the user to participate in a heavily scripted dialogue with constrained text en

Complexity-weighted loss and diverse reranking for sentence simplification
R Kriz, J Sedoc, M Apidianaki, C Zheng… – arXiv preprint arXiv …, 2019 – arxiv.org
… approach is to use sequence-to-sequence (Seq2Seq) models, which have shown state-of- the-art performance on a variety of NLP tasks, in- cluding machine translation (Vaswani et al., 2017) and dialogue systems (Vinyals and … (2018), we train a linear regression model using …

Bandit Algorithms to Personalize Educational Chatbots
W Cai, J Grossman, ZJ Lin, H Sheng, JTZ Wei… – 2020 – 5harad.com
… 3 Page 4. Chatbots. Chatbots have been widely applied to various domains, such as customer service [47], college management [7], and purchase recommendation [22]. One approach to building a chatbot is to construct rule-based input-to-output mappings [2, 48] …

EMMA: An Emotion-Aware Wellbeing Chatbot
A Ghandeharioun, D McDuff… – 2019 8th …, 2019 – ieeexplore.ieee.org
… Synchronous, text-based interventions, either by a human or a chat-bot, have shown … the BL quadrant of Russel’s circumplex model of emotion, the chatbot would recommend … and experimented with a range of regression models including Linear Regression, several regularized …

Artificial intelligence in education
W Holmes, M Bialik, C Fadel – Boston: Center for …, 2019 – curriculumredesign.org
… will confirm, AIED includes everything from AI-driven, step-by-step personalized instructional and dialogue systems, through AI-supported exploratory learning, the analysis of student writing, intelligent agents in game-based environments, and student-support chatbots, to AI …

Estimating Conversational Styles in Conversational Microtask Crowdsourcing
S Qiu, U Gadiraju, A Bozzon – Proceedings of the ACM on Human …, 2020 – dl.acm.org
… called Guardian, which enables both expert and non-expert workers to collaboratively translate Web APIs into a dialogue system format [22] … A previous work based on Facebook Messenger used a Chatbot to connect learners and experts, for providing experts’ feedback to …

Clinical Screening Interview Using a Social Robot for Geriatric Care
HM Do, W Sheng, EE Harrington… – IEEE Transactions on …, 2020 – ieeexplore.ieee.org
… screening interviews. Dialogue systems or conversational agents have many appli- cations [29] and are generally classified into three main types: task-oriented dialogue agents, chatbots, and QA systems. Task- oriented dialogue …

Dynamic emotion modelling and anomaly detection in conversation based on emotional transition tensor
X Sun, C Zhang, L Li – Information Fusion, 2019 – Elsevier
… 9], vehicle-mounted systems[10], smart homes [11] and chatbots [12], such … Chatbot content usually comes from a corpus of web-based knowledge, the answer … univariate location and scale, multivariate location and covariance estimation, linear regression, principal component …

Novel Computational Linguistic Measures, Dialogue System and the Development of SOPHIE: Standardized Online Patient for Healthcare Interaction Education
MR Ali, T Sen, B Kane, S Bose, TM Carroll… – arXiv preprint arXiv …, 2020 – arxiv.org
… Novel Computational Linguistic Measures, Dialogue System and the Development of SOPHIE: Standardized Online Patient for Healthcare … Our outcome variable is binary (either you understand or don’t understand your prognosis), therefore instead of linear regression we use …

Offline reinforcement learning: Tutorial, review, and perspectives on open problems
S Levine, A Kumar, G Tucker, J Fu – arXiv preprint arXiv:2005.01643, 2020 – arxiv.org
Page 1. Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems Sergey Levine1,2, Aviral Kumar1, George Tucker2, Justin Fu1 1UC Berkeley, 2Google Research, Brain Team Abstract In this tutorial …

Cooperation in online conversations: the response times as a window into the cognition of language processing
B Jacquet, J Baratgin, F Jamet – Frontiers in psychology, 2019 – frontiersin.org
… anxiety on a daily basis, like Woebot 1 or Tess 2 . These agents often take the shape of chatterbots (or chatbots): they are … We used a multiple linear regression model (with interaction) between the length of the experimenter’s utterance and the length of the participant’s reply on …

The bottleneck simulator: A model-based deep reinforcement learning approach
IV Serban, C Sankar, M Pieper, J Pineau… – Journal of Artificial …, 2020 – jair.org
Page 1. Journal of Artificial Intelligence Research 69 (2020) 571-612 Submitted 07/2018; published 10/2020 The Bottleneck Simulator: A Model-Based Deep Reinforcement Learning Approach Iulian Vlad Serban iulian.vlad.serban@umontreal.ca Chinnadhurai Sankar …

Era of Intelligent Systems in Healthcare
S Belciug, F Gorunescu – … Decision Support Systems—A Journey to …, 2020 – Springer
The aim of this chapter is to prepare the reader for the outstanding trip that she/he embarked when starting reading this book. At first, we shall try to look for answers to some of the most…

DEEP NEURAL NETWORD BASED NATURAL LANGUAGE INFERENCE MODEL
T BEKELE – 2020 – ir.bdu.edu.et
… NLU services in different domains and for different purposes, eg natural language inference [23], question answering for localized search [24], form driven dialogue systems [25], dialogue management [26], and the internet of things [27]. 2.2 …

Learning to merge-language and vision: A deep evaluation of the encoder, the role of the two modalities, the role of the training task.
R Shekhar – 2019 – eprints-phd.biblio.unitn.it
… by the encoder as general purposed representations. We have proposed and an- alyzed a cognitive plausible architecture in which dialogue system modules are connected through a common grounded dialogue state encoder. Our in-depth …

Evaluation of Methods for Data-Driven Tools that Empower Mental Health Professionals
OA Demasi – 2019 – escholarship.org
… P-values for linear regression coefficients are in parenthesis, where appropriate, and * denotes values less than 0.001 … Some organizations are considering the use of automated interactive agents (chatbots) to replace human counselors …

Joint learning of question answering and question generation
Y Sun, D Tang, N Duan, T Qin, S Liu… – … on Knowledge and …, 2019 – ieeexplore.ieee.org
… There are different kinds of QA tasks in the natural lan- guage processing area. We consider answer selection as our QA task, which is fundamental QA tasks in research com- munity and of great importance in industrial applications including web search and chatbot …

Artificial Intelligence in Education
S Isotani, E Millán, A Ogan, P Hastings, B McLaren… – 2019 – Springer
Page 1. Seiji Isotani · Eva Millán · Amy Ogan · Peter Hastings · Bruce McLaren · Rose Luckin (Eds.) 123 LNAI 11626 20th International Conference, AIED 2019 Chicago, IL, USA, June 25-29, 2019 Proceedings, Part II Artificial Intelligence in Education Page 2 …

Suicidal ideation detection in online social content
S Ji – 2020 – researchgate.net
… which was later discontinued because of privacy issues. The latter is a Facebook chatbot based on cognitive behavioral therapy and natural language processing (NLP) techniques for relieving people’s depression and anxiety …

Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings
S Isotani, E Millán, A Ogan, P Hastings, B McLaren… – 2019 – books.google.com
Page 1. Seiji Isotani· Eva Millán · Amy Ogan · Peter Hastings· Bruce McLaren · Rose Luckin (Eds.) Artificial Intelligence in Education 20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part I 123 Page 2 …

Talker Quality in Human and Machine Interaction
B Weiss – 2020 – Springer
… implementation [216]. Of course, with the recent developments in, eg, smartphone applications and chat bots, developing innovative small-scale services that fulfill everyday needs are entangled strongly with the interface concept. In …

Understanding and generating language with abstract meaning representation
M Damonte – 2020 – era.ed.ac.uk
… dren: HAL and door. The output of the NLU component is then passed into an inference component (for example, a dialogue manager in dialogue systems and chatbots), which can reason over the meaning representation. The output …

A multimodal approach to sarcasm detection on social media
D Das – 2019 – researchgate.net
… of sarcasm Page 89 7.3 Top five objects that were closest to the gaze center points Page 90 8.1 Sample positive and negative reviews, and replies from chatbot-based auto-replier sys- tem. Page 94 8.2 Inappropriate response from auto-replier for a multimodal sarcastic review …

Intelligent Decision Support Systems: A Journey to Smarter Healthcare
S Belciug, F Gorunescu – 2020 – Springer
… Chapter 4 then proceeds to describe the main areas of data mining-based intelligent decision support systems: multiple linear regression, logistic regression, the softmax classifier, artificial neural networks, evolutionary-driven ML paradigm, and queuing models, while Chap …

Crsal: Conversational recommender systems with adversarial learning
X Ren, H Yin, T Chen, H Wang, NQV Hung… – ACM Transactions on …, 2020 – dl.acm.org
… 2.2 Task-oriented Dialogue Systems Task-oriented dialogue systems are one important branch in dialogue system research, which aims to help the user finish some specific tasks [96]. Though conversational recommendation …

Artificial Intelligence in Management: Self-learning and Autonomous Systems as Key Drivers of Value Creation
A Wodecki – 2020 – books.google.com
… M. (2020), Deep Reinforcement Learning Hands-On: Apply Modern RL Methods to Practical Problems of Chatbots, Robotics, Discrete … The most important algorithms (or ‘teaching methods’) used in supervised learning include linear regression, logical regression, support vector …

Deep learning based recommender system: A survey and new perspectives
S Zhang, L Yao, A Sun, Y Tay – ACM Computing Surveys (CSUR), 2019 – dl.acm.org
… matrix factorization models the user/item interaction by linearly combining user and item latent factors [53]; Factorization machines are members of the multivariate linear family [54]; Obviously, SLIM is a linear regression model with … speech recognition, chatbots, and many others …