Quality Assessment Methods for Textual Conversational Interfaces: A Multivocal Literature Review

The evaluation and assessment of conversational interfaces is a complex task since such software products are challenging to validate through traditional testing approaches. We conducted a systematic Multivocal Literature Review (MLR), on five different literature sources, to provide a view on quality attributes, evaluation frameworks, and evaluation datasets proposed to provide aid to the researchers and practitioners of the field. We came up with a final pool of 118 contributions, including grey (35) and white literature (83). We categorized 123 different quality attributes and metrics under ten different categories and four macro-categories: Relational, Conversational, User-Centered and Quantitative attributes. While Relational and Conversational attributes are most commonly explored by the scientific literature, we testified a predominance of User-Centered Attributes in industrial literature. We also identified five different academic frameworks/tools to automatically compute sets of metrics, and 28 datasets (subdivided into seven different categories based on the type of data contained) that can produce conversations for the evaluation of conversational interfaces. Our analysis of literature highlights that a high number of qualitative and quantitative attributes are available in the literature to evaluate the performance of conversational interfaces. Our categorization can serve as a valid entry point for researchers and practitioners to select the proper functional and non-functional aspects to be evaluated for their products.

aCHAT-WF: Generating conversational agents for teaching business process models

This paper proposes a general approach for using conversational interfaces such as chatbots to offer adaptive learning of business processes in an environment involving different actors. Adaptivity concerns both the content being proposed, the sequence of learning items, and the way the conversation is conducted. The original approach allows the development of sustainable chatbots and empowers various non-technical actors (authors, teachers, publishers, and learners) to control the chatbot features directly. The aCHAT-WF framework (adaptive CHATbot for WorkFlows), proposed in this paper for managing conversational interfaces, conceptually represents all the aspects related to a conversation about business processes, with different facets for the user, the conversation flow, and the conversation contents, combining them to obtain a flexible interaction with the user. The paper focuses on the different preparation phases for instructional material based on Business Process Modeling Notation (BPMN) models, separating the different roles involved in the construction of a chatbot for teaching business processes and with the possibility of defining different styles for the interaction with the users. The proposed method is configuration-driven, to facilitate the separation of the different aspects of the control of the interaction and the delivery of contents.

The Use and Promise of Conversational Agents in Digital Health

Summary Objectives: To describe the use and promise of conversational agents in digital health—including health promotion andprevention—and how they can be combined with other new technologies to provide healthcare at home. Method: A narrative review of recent advances in technologies underpinning conversational agents and their use and potential for healthcare and improving health outcomes. Results: By responding to written and spoken language, conversational agents present a versatile, natural user interface and have the potential to make their services and applications more widely accessible. Historically, conversational interfaces for health applications have focused mainly on mental health, but with an increase in affordable devices and the modernization of health services, conversational agents are becoming more widely deployed across the health system. We present our work on context-aware voice assistants capable of proactively engaging users and delivering health information and services. The proactive voice agents we deploy, allow us to conduct experience sampling in people's homes and to collect information about the contexts in which users are interacting with them. Conclusion: In this article, we describe the state-of-the-art of these and other enabling technologies for speech and conversation and discuss ongoing research efforts to develop conversational agents that “live” with patients and customize their service offerings around their needs. These agents can function as ‘digital companions’ who will send reminders about medications and appointments, proactively check in to gather self-assessments, and follow up with patients on their treatment plans. Together with an unobtrusive and continuous collection of other health data, conversational agents can provide novel and deeply personalized access to digital health care, and they will continue to become an increasingly important part of the ecosystem for future healthcare delivery.

Conversational interfaces for unconventional access to business relational data structures

PurposeCurrent possibilities of accessing business data by regular users usually involve complicated user interfaces or require technical expertise. This results in situations when business owners are separated from their data. The aim of this research is to apply an innovative approach leveraging conversational interfaces to tackle this problem.Design/methodology/approachThe authors examine the current possibilities of accessing business data by business, users with an emphasis on conversational interfaces employing a chatbot as an alternative to traditional approaches. The authors propose a new concept relying on a guided conversation, and through experiments with a real chatbot and database, the authors demonstrate the benefits of the proposed approach.FindingsThe authors found out that the key to the success of our approach is a decomposition of complex database queries and their incremental construction in conversations. This also enables natural discovery of the domain model through constantly provided feedback. Based on the experiments with a real chatbot, the authors demonstrate that defining conversation flows and maintaining the conversation context is a crucial aspect contributing to the overall accuracy, together with keeping the conversation within the defined limits in its certain parts.Originality/valueThe authors present a novel approach using natural language interfaces for accessing data by business users. In contrast to existing approaches, the authors emphasize incremental construction of queries, predefined conversation flows and constraining the conversations, when necessary.

Implementation of Embedded Unspecific Continuous English Speech Recognition Based on HMM

Background: Human-computer interaction plays a vital role through Natural Language Conversational Interfaces to improve the usage of computers. Speech recognition technology allows the machine to understand human language. A speech recognition algorithm is used to achieve this function. Methodology: This paper is mainly based on the fundamental theoretical research of speech signals, establishes the HMM model, uses speech collection, recognition, and other methods, simulates on MATLAB, and integrates the recognition system ported to ARM for debugging and running to realize the embedded speech recognition function based on HMM under the ARM platform. Conclusion: The conclusion shows that the HMM-based embedded unspecific continuous English speech recognition system has high recognition accuracy and fast speed.

J.A.R.V.I.S Organizational Virtual Assistant

Chatbots, or conversational interfaces as they are also known, present a new way for individuals to interact with computer systems. Traditionally, to get a question answered by a software program involved using a search engine, or filling out a form. A chatbot allows a user to simply ask questions in the same manner that they would address a human. The most well known chatbots currently are voice chatbots: Alexa and Siri. However, chatbots are currently being adopted at a high rate on computer chat platforms. The technology at the core of the rise of the chatbot is natural language processing (“NLP”). A simple chatbot can be created by loading an FAQ (frequently asked questions) into chatbot software. The functionality of the chatbot can be improved by integrating it into the organization’s enterprise software, allowing more personal questions to be answered, like“When is the meet?”, or “What is the schedule of my day?”. A chatbot can be used as an “assistant” to a live agent, increasing the agent’s efficiency.

The Voice Enabled Personal Assistant for Pc using Python

Personal Assistants, or conversational interfaces, or chat bots reinvent a new way for individuals to interact with computes. A Personal Virtual Assistant allows a user to simply ask questions in the same manner that they would address a human, and are even capable of doing some basic tasks like opening apps, reading out news, taking notes etc., with just a voice command. Personal Assistants like Google Assistant, Alexa, Siri works by Speech Recognition (Speech-to-text) and Text-to-Speech.

Resonance as an Applied Predictor of Cross-Cultural Interaction: Constructional Priming in Mandarin and American English Interaction

In Dialogic syntax (cf. Du Bois 2014; Tantucci et al. 2018), naturalistic interaction is inherently grounded in resonance, viz. the catalytic activation of affinities across turns (Du Bois and Giora 2014). Resonance occurs dynamically when interlocutors creatively coconstruct utterances that are formally and phonetically similar to the utterance of a prior speaker. In this study, we argue that such similarity can inform the machine learning prediction of linguistic and cross-cultural diversity. We compared two sets of 1,000 exchanges involving (dis)-agreement from the two balanced Callhome corpora of naturalistic interaction in Mandarin Chinese and American English. We found a correlation of overt use of pragmatic markers with resonance, indicating that priming does not occur as an exclusively implicit mechanism (as it is commonly held in the experimental literature e.g. Bock 1986; Bock et al. 2007), but naturalistically underpins dialogic engagement and cooperation among interactants. We fitted a mixed effects linear regression and a hierarchical clustering model to show that resonance occurs formally and functionally in different ways from one language to another. The applied results of this study can lead to a novel turn in AI research of conversational interfaces (McTear et al. 2016; Klopfenstein et al. 2017), as they reveal the fundamental role played cross-linguistically by resonance as a form of engagement of human-to-human interaction and the importance to address this mechanism in machine-to-human communication.

Brokerbot: A Cryptocurrency Chatbot in the Social-technical Gap of Trust

Cryptocurrencies are proliferating as instantiations of blockchain, which is a transparent, distributed ledger technology for validating transactions. Blockchain is thus said to embed trust in its technical design. Yet, blockchain’s technical promise of trust is not fulfilled when applied to the cryptocurrency ecosystem due to many social challenges stakeholders experience. By investigating a cryptocurrency chatbot (Brokerbot) that distributed information on cryptocurrency news and investments, we explored social tensions of trust between stakeholders, namely the bot’s developers, users, and the bot itself. We found that trust in Brokerbot and in the cryptocurrency ecosystem are two conjoined, but separate challenges that users and developers approached in different ways. We discuss the challenging, dual-role of a Brokerbot as an object of trust as a chatbot while simultaneously being a mediator of trust in cryptocurrency, which exposes the social-technical gap of trust. Lastly, we elaborate on trust as a negotiated social process that people shape and are shaped by through emerging ecologies of interlinked technologies like blockchain and conversational interfaces.