A Dialogue-Act Taxonomy for a Virtual Coach Designed to Improve the Life of Elderly

This paper presents a dialogue act taxonomy designed for the development of a conversational agent for elderly. The main goal of this conversational agent is to improve life quality of the user by means of coaching sessions in different topics. In contrast to other approaches such as task-oriented dialogue systems and chit-chat implementations, the agent should display a pro-active attitude, driving the conversation to reach a number of diverse coaching goals. Therefore, the main characteristic of the introduced dialogue act taxonomy is its capacity for supporting a communication based on the GROW model for coaching. In addition, the taxonomy has a hierarchical structure between the tags and it is multimodal. We use the taxonomy to annotate a Spanish dialogue corpus collected from a group of elder people. We also present a preliminary examination of the annotated corpus and discuss on the multiple possibilities it presents for further research.The research presented in this paper is conducted as part of the project EMPATHIC that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769872. The authors would also like to thank the support by the Basque Government through the project IT-1244-19

Identifying interaction types and functionality for automated vehicle virtual assistants: An exploratory study using speech acts cluster analysis

Onboard virtual assistants with the ability to converse with users are gaining favour in supporting effective human-machine interaction to meet safe standards of operation in automated vehicles (AVs). Previous studies have highlighted the need to communicate situation information to effectively support the transfer of control and responsibility of the driving task. This study explores ‘interaction types’ used for this complex human-machine transaction, by analysing how situation information is conveyed and reciprocated during a transfer of control scenario. Two human drivers alternated control in a bespoke, dual controlled driving simulator with the transfer of control being entirely reliant on verbal communication. Handover dialogues were coded based on speech-act classifications, and a cluster analysis was conducted. Four interaction types were identified for both virtual assistants (i.e., agent handing over control) - Supervisor, Information Desk, Interrogator and Converser, and drivers (i.e., agent taking control) - Coordinator, Perceiver, Inquirer and Silent Receiver. Each interaction type provides a framework of characteristics that can be used to define driver requirements and implemented in the design of future virtual assistants to support the driver in maintaining and rebuilding timely situation awareness, whilst ensuring a positive user experience. This study also provides additional insight into the role of dialogue turns and takeover time and provides recommendations for future virtual assistant designs in AVs

Detecting the Intent of Email Using Embeddings, Deep Learning and Transfer Learning

Throughout the years\u27 several strategies and tools were proposed and developed to help the users cope with the problem of email overload, but each of these solutions had its own limitations and, in some cases, contribute to further problems. One major theme that encapsulates many of these solutions is automatically classifying emails into predefined categories (ex: Finance, Sport, Promotion, etc.) then move/tag the incoming email to that particular category. In general, these solutions have two main limitations: 1) they need to adapt to changing user’s behavior. 2) they require handcrafted features engineering which in turn need a lot of time, effort, and domain knowledge to produce acceptable performance.This dissertation aims to explore the email phenomenon and provide a scalable solution that addresses the above limitations. Our proposed system requires no handcrafted features engineering and utilizes the Speech Act Theory to design a classification system that detects whether an email required an action (i.e. to do) or no action (i.e. to read). We can automate both the features extraction and the classification phases by using our own word embeddings, trained on the entire Enron Email dataset, to represent the input. Then, we use a convolutional layer to capture local tri-gram features, followed by an LSTM layer to consider the meaning of a given feature (trigrams) concerning some “memory” of words that could occur much earlier in the email. Our system detects the email intent with 89% accuracy outperforming other related works. In developing this system, we followed the concept of Occam’s razor (i.e. law of parsimony). It is a problem-solving principle stating that entities should not be multiplied without necessity. Chapter four present our efforts to simplify the above-proposed model by dropping the use of the CNN layer and showing that fine-tuning a pre-trained Language Model on the Enron email dataset can achieve comparable results. To the best of our knowledge, this is the first attempt of using transfer learning to develop a deep learning model in the email domain. Finally, we showed that we could even drop the LSTM layer by representing each email’s sentences using contextual word/sentence embeddings. Our experimental results using three different types of embeddings: context-free word embeddings (word2vec and GloVe), contextual word embeddings (ELMo and BERT), and sentence embeddings (DAN-based Universal Sentence Encoder and Transformer-based Universal Sentence Encoder) suggest that using ELMo embeddings produce the best result. We achieved an accuracy of 90.10%, comparing with word2vec (82.02%), BERT (58.08%), DAN-based USE (86.66%), and Transformer-based USE (88.16%)

How Will Drivers and Passengers Interact in Future Automated Vehicles?

The presence of one or more passengers has been shown to distract drivers during manual driving, with reported reductions in situational awareness, an increase in the risk of taking unsafe actions, and an increased risk of a fatal crash, particularly in the case of young drivers. However, the presence of a passenger during Society of Automotive Engineers (SAE) Level 3 conditional driving automation (SAE, 2021) has, to date, received no empirical attention. Building on previous studies funded by the RAC Foundation (Burnett et al., 2019; Shaw et al., 2020), we invited 18 driver/passenger pairings (12 of the passengers in which were also themselves qualified and experienced drivers) to undertake three authentic journeys in the Human Factors Research Group’s driving simulator at the University of Nottingham. As before, SAE Level 3 conditional driving automation was activated on the motorway, and drivers and passengers were free to undertake any activities they deemed acceptable while the vehicle was in control, with the aim of preserving important motivational aspects. Inspired by our previous work, the research questions posed by the current study were:1.What will drivers and passengers naturally do in future automated vehicles?2.What impact does the presence of a passenger have on the driving task – that is, during periods of automation and also during the resumption of the driving task?3.How does the presence of a passenger affect levels of situational awareness, workload, trust and acceptance

Classifying speech acts using verbal response modes

The driving vision for our work is to provide intelligent, automated assistance to users in understanding the status of their email conversations. Our approach is to create tools that enable the detection and connection of speech acts across email messages. We thus require a mechanism for tagging email utterances with some indication of their dialogic function. However, existing dialog act taxonomies as used in computational linguistics tend to be too task- or application-specific for the wide range of acts we find represented in email conversation. The Verbal Response Modes (VRM) taxonomy of speech acts, widely applied for discourse analysis in linguistics and psychology, is distinguished from other speech act taxonomies by its construction from crosscutting principles of classification, which ensure universal applicability across any domain of discourse. The taxonomy categorises on two dimensions, characterised as literal meaning and pragmatic meaning. In this paper, we describe a statistical classifier that automatically identifies the literal meaning category of utterances using the VRM classification. We achieve an accuracy of 60.8% using linguistic features derived from VRM’s human annotation guidelines. Accuracy is improved to 79.8% using additional features.8 page(s

Identifying Stylometric Correlates of Social Power

This thesis takes a stylometric approach to the measurement of social power, particularly hierarchical power in an organisational setting. Following the social constructionist view of identity, we infer that construction of identity is an ongoing process incorporating the full scope of human behaviour, including linguistic behaviour. We test the primary hypothesis that stylistic choice in language is indicative of power relations, and that a stylometric signal can be extracted from natural language to enable prediction of relationship status. Additionally, we consider the effect of individual variation versus interpersonal variation, and the effects of aggregating predictions to boost the predictive strength of the model. Three different datasets are used to validate the proposed approach across three different genres: email, spoken conversation, and online chat. We also present a vector space approach to modelling linguistic style accommodation, and undertake a preliminary examination of the correlation between linguistic accommodation and social power