Confusion Modelling - An Estimation by Semantic Embeddings

Approaching the task of coherence assessment of a conversation from its negative perspective ‘confusion’ rather than coherence itself, has been attempted by very few research works. Influencing Embeddings to learn from similarity/dissimilarity measures such as distance, cosine similarity between two utterances will equip them with the semantics to differentiate a coherent and an incoherent conversation through the detection of negative entity, ‘confusion’. This research attempts to measure coherence of conversation between a human and a conversational agent by means of such semantic embeddings trained from scratch by an architecture centralising the learning from the distance between the embeddings. State of the art performance of general BERT’s embeddings and state of the art performance of ConveRT’s conversation specific embeddings in addition to the GLOVE embeddings are also tested upon the laid architecture. Confusion, being a more sensible entity, real human labelling performance is set as the baseline to evaluate the models. The base design resulted in not such a good performance against the human score but the pre-trained embeddings when plugged into the base architecture had performance boosts in a particular order from lowest to highest, through BERT, GLOVE and ConveRT. The intuition and the efficiency of the base conceptual design is proved of its success when the variant having the ConveRT embeddings plugged into the base design, outperformed the original ConveRT’s state of art performance on generating similarity scores. Though a performance comparable to real human performance was not achieved by the models, there witnessed a considerable overlapping between the ConveRT variant and the human scores which is really a great positive inference to be enjoyed as achieving human performance is always the state of art in any research domain. Also, from the results, this research joins the group of works claiming BERT to be unsuitable for conversation specific modelling and embedding works

Social and Semantic Contexts in Tourist Mobile Applications

The ongoing growth of the World Wide Web along with the increase possibility of access information through a variety of devices in mobility, has defi nitely changed the way users acquire, create, and personalize information, pushing innovative strategies for annotating and organizing it. In this scenario, Social Annotation Systems have quickly gained a huge popularity, introducing millions of metadata on di fferent Web resources following a bottom-up approach, generating free and democratic mechanisms of classi cation, namely folksonomies. Moving away from hierarchical classi cation schemas, folksonomies represent also a meaningful mean for identifying similarities among users, resources and tags. At any rate, they suff er from several limitations, such as the lack of specialized tools devoted to manage, modify, customize and visualize them as well as the lack of an explicit semantic, making di fficult for users to bene fit from them eff ectively. Despite appealing promises of Semantic Web technologies, which were intended to explicitly formalize the knowledge within a particular domain in a top-down manner, in order to perform intelligent integration and reasoning on it, they are still far from reach their objectives, due to di fficulties in knowledge acquisition and annotation bottleneck. The main contribution of this dissertation consists in modeling a novel conceptual framework that exploits both social and semantic contextual dimensions, focusing on the domain of tourism and cultural heritage. The primary aim of our assessment is to evaluate the overall user satisfaction and the perceived quality in use thanks to two concrete case studies. Firstly, we concentrate our attention on contextual information and navigation, and on authoring tool; secondly, we provide a semantic mapping of tags of the system folksonomy, contrasted and compared to the expert users' classi cation, allowing a bridge between social and semantic knowledge according to its constantly mutual growth. The performed user evaluations analyses results are promising, reporting a high level of agreement on the perceived quality in use of both the applications and of the speci c analyzed features, demonstrating that a social-semantic contextual model improves the general users' satisfactio

Adapting Agent Platforms to Web Service Environments

This master thesis tries to address the above-mentioned issues by creating an agent plat- form suitable for encapsulating web-services into agents, providing them with typical agent capabilities (such as learning or complex communication and reasoning mechanisms). The objective of this point is to create a generic, modular agent platform that is able to run lightweight agents. The agents should be able to easily invoke web-services, e ectively encapsulating them. They also should be able to easily coordinate for composing the invoked services in order to perform complex tasks. Thus, the platform must provide facilities to allow the agents perform these service invocations

Designing brand chatbots: The impact of chatbot’s personality on the user’s brand personality perception

Along with advancements in technologies, which include machine learning and artificial intelligence, chatbots are increasingly taking the place of employees that work as customer service agents and personal shoppers. Considering that the characteristics of employees can influence a consumer’s perception of brand personality (Aaker, 1997), this perception may also be affected by the chatbot’s personality. This paper aims to investigate the impact of a chatbot’s personality on a user’s perception of brand personality. Two brands, and their chatbots, are used as case studies. The empirical study comprises of two stages, in which the qualitative and the quantitative data are both gathered and analyzed. Firstly, an online survey was conducted to investigate the personalities of two existing brands and their respective chatbots. As a result, a gap in personality between one of the brands and its chatbot was identified. Next, two prototypes were built and then tested in the interview. One was the emulator of the current brand chatbot, and the other was a new chatbot designed to have a personality closer to the brand personality. The findings reveal that the chatbot’s personality may affect brand personality, even though the impact was smaller than expected because participants perceived that the two prototypes’ personalities were moderately close to the brand personality. Interestingly, interviewees revealed that the chatbot’s personality may have a greater influence if it is totally different from the brand personality. Based on the study findings, design considerations are suggested to help practitioners in designing brand chatbots

The CHROME Manifesto: integrating multimodal data into Cultural Heritage Resources

The CHROME Project aims at collecting a wide portfolio of digital resources oriented to technological application in Cultural Heritage (henceforth CH). The contributions for the realisation of such objective come from the efforts of computer scientists, psychologists, architects, and computational linguists, who constitute an interdisciplinary equipe. We are collecting and analyzing texts, spoken materials, architectural surveys, and human motion videos, attempting the integration of these data in a multidimensional platform based on multilevel annotation systems, game engines importing, and virtualization techniques. As case of study we choose to work on the magic travel along three Charterhouses located in Campania region: S. Martino in Naples, S. Lorenzo in Padula (Salerno) and S. Giacomo, in Capri.Il progetto CHROME (Cultural Heritage Resources Orienting Multimodal Experiences – PRIN 2015 MIUR) si pone come scopo la raccolta di una ampia gamma di risorse digitali da utilizzare in applicazione tecnologiche per il miglioramento della fruizione dei beni culturali (CH). A questo obiettivo concorrono interdisciplinarmente informatici, psicologi, architetti, linguisti che collezionano testi, registrazioni di parlato, rilievi architettonici, video e human motion capture. Questi dati sono poi integrati in una piattaforma nella quale è possibile effettuare una annotazione multidimensionale, sono anche utilizzati per la virtualizzazione di ambienti tridimensionali e il porting in ambienti di gaming