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Alessandro Dell'Orto

Bio: Alessandro Dell'Orto is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Chatbot. The author has an hindex of 1, co-authored 1 publications receiving 16 citations.
Topics: Chatbot

Papers
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Proceedings ArticleDOI
28 May 2018
TL;DR: Requirement and design options that directly put the user into control of their own personal bot that is personal and helpful by providing services that are chosen and configured by the users themselves, for themselves are discussed.
Abstract: Chatbots, i.e., conversational software agents able to interact with users via instant messaging channels like Messenger, WhatsApp or SMS, have the power to substantively simplify human-computer interaction thanks to their natural language paradigm. While this certainly helps to lower barriers, state-of-the-art chatbots prevalently provide access to generic, non-personalized features with relatively little usefulness. This may hinder adoption. To provide users with real value, we envision a kind of chatbot that is personal and helpful by providing services that are chosen and configured by the users themselves, for themselves. As the development of a one-size-fits-all, yet flexible and customizable bot is hard, if not impossible, we discuss requirements and design options that directly put the user into control of their own personal bot.

25 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, the authors describe how tourist companies are taking advantage of modern technologies, such as Chatbots, by adopting them in hotels, travel agencies, and airline companies. Despite t...
Abstract: As with other businesses, tourist companies are taking advantage of modern technologies. Chatbots are a recent technology that hotels, travel agencies, and airline companies are adopting. Despite t...

105 citations

Journal ArticleDOI
TL;DR: IntelliBot is proposed, which is a response-generating dialogue-based chatbot that uses multiple strategies to generate a response and demonstrates IntelliBot’s superiority in engaging with the user and providing a complete answer in the insurance domain.
Abstract: Chatbots have become the go-to platform for users to receive answers to their queries. They are now being used by many businesses too to provide their customers with a virtual assistant to answer their queries. But when it comes to engaging with a user in a dialogue, existing chatbots have several shortcomings, with issues such as failing to provide a meaningful response to the user, offering semantically incorrect information etc. This paper studies the working styles of existing chatbots in generating a response and then identifies their shortcomings from the viewpoint of engaging in a dialogue with a user. It then proposes a domain-specific chatbot named IntelliBot, which is a response-generating dialogue-based chatbot that uses multiple strategies to generate a response. IntelliBot was trained on two datasets, namely the Cornell movie dialogue and a custom-built insurance dataset so it has domain-specific knowledge. The performance of IntelliBot was then validated and compared with three other chatbots from the literature, namely RootyAI, ChatterBot and DeepQA. The results demonstrate IntelliBot’s superiority in engaging with the user and providing a complete answer in the insurance domain.

39 citations

Journal Article
TL;DR: This review engages in three questions that surround this issue: why are chatbots not already at the centre of foreign language learning, and what are two key developers of chatbots working towards that might push chatbots into the language learning spotlight.
Abstract: Bots are destined to dominate how humans interact with the internet of things that continues to grow around them. Despite their still budding intellectual capacity, major companies (e.g., Apple, Google and Amazon) have already placed (chat)bots at the centre of their flagship devices. (Chat)Bots currently fill the internet acting as guides, merchants and assistants. Chatbots, designed as communicators, however, have yet to make a meaningful contribution to perhaps their most natural vocation: foreign language learning partners. This review engages in three questions that surround this issue: 1. Why are chatbots not already at the centre of foreign language learning? 2. What are two key developers of chatbots working towards that might push chatbots into the language learning spotlight? 3. What might researchers, educators, and developers together do to support chatbots as foreign language learning partners right now?

31 citations

Proceedings ArticleDOI
06 Sep 2020
TL;DR: How users currently perceive CAs is revealed and quality criteria that could inform their future design are identified to contribute to the field by extending these guidelines from an end-user's perspective.
Abstract: Conversational agents (CAs) such as Siri, Alexa, and Google Assistant are increasingly penetrating everyday life. From a Human-Computer Interaction (HCI) perspective, designing CAs that appropriately support the way they are used within daily life is still challenging. While initial design guidelines for human-AI interaction exist, we still know little about how users actually perceive CAs within their daily lives and what aspects motivate their usage of such tools. Within our research, we therefore conducted an interview study with 29 participants to uncover daily positive and negative experiences with CAs. By revealing how users currently perceive CAs, we identify quality criteria that could inform their future design. By evaluating these criteria with respect to existing research discourses about user experience (UX) guidelines for CAs, we contribute to the field by extending these guidelines from an end-user's perspective.

19 citations

Proceedings ArticleDOI
28 Feb 2022
TL;DR: It can be concluded that the developed chatbot to assess the risk from occupational diseases among informal workers and to provide knowledge on disease prevention can be used in actual practice.
Abstract: This research aimed to study and develop a chatbot to assess the risk from occupational diseases among informal workers and to provide knowledge on disease prevention. There is a problem of accessing health services among informal workers or independent professional groups who do not have health benefit, lack professional healthcare, and lack knowledge about risk factors arising from their work. In addition, visiting a doctor in person takes time and informal workers may lose benefit from the work they do when doing so. Therefore, the researcher applied artificial intelligence to seek answers according to word groups based on word weight that classified symptoms, disease risk, and disease prevention guidelines for each occupation from diagnostic manuals. The system was developed to be compatible with the LINE application that is easily accessible. The evaluation of the system accuracy by experts had a total value of 86%. The evaluation of the overall system quality in 3 aspects had an average value of 4.24. It can be concluded that the developed system can be used in actual practice.

8 citations