Sentiment and behaviour annotation in a corpus of dialogue summaries

This paper proposes a scheme for sentiment annotation. We show how the task can be made tractable by focusing on one of the many aspects of sentiment: sentiment as it is recorded in behaviour reports of people and their interactions. Together with a number of measures for supporting the reliable application of the scheme, this allows us to obtain sufficient to good agreement scores (in terms of Krippendorf's alpha) on three key dimensions: polarity, evaluated party and type of clause. Evaluation of the scheme is carried out through the annotation of an existing corpus of dialogue summaries (in English and Portuguese) by nine annotators. Our contribution to the field is twofold: (i) a reliable multi-dimensional annotation scheme for sentiment in behaviour reports; and (ii) an annotated corpus that was used for testing the reliability of the scheme and which is made available to the research community

Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling changed

Towards an ISO Standard for Dialogue Act Annotation

This paper describes an ISO project developing an international standard for annotating dialogue with semantic information, in particular concerning the communicative functions of the utterances, the kind of content they address, and the dependency relations to what was said and done earlier in the dialogue. The project, registered as ISO 24617-2 Semantic annotation framework, Part 2: Dialogue acts”, is currently at DIS stage. 1

Using Technology to Encourage Self-Directed Learning: The Collaborative Lecture Annotation System

The rapidly-developing 21st century world of work and knowledge calls for self-directed lifelong (SDL) learners. While higher education must embrace the types of pedagogies that foster SDL skills in graduates, the pace of change in education can be glacial. This paper describes a social annotation technology, the Collaborative Lecture Annotation System (CLAS), that can be used to leverage existing teaching and learning practices for acquisition of 21st Century SDL skills. CLAS was designed to build upon the artifacts of traditional didactic modes of teaching, create enriched opportunities for student engagement with peers and learning materials, and offer learners greater control and ownership of their individual learning strategies. Adoption of CLAS creates educational experiences that promote and foster SDL skills: motivation, self-management and self-monitoring. In addition, CLAS incorporates a suite of learning analytics for learners to evaluate their progress, and allow instructors to monitor the development of SDL skills and identify the need for learning support and guidance. CLAS stands as an example of a simple tool that can bridge the gap between traditional transmissive pedagogy and the creation of authentic and collaborative learning spaces

NEW CONCEPTS AND MEANINGS OF SLOW. The case of Slow Art

The present study explores new meanings and values of the word slow in the context of Slow Art Day, a global event that takes place once a year and whose aim is to encourage both visitors and museum curators to engage with art in new and different ways. Since 1989 and the early days of Carlo Petrini’s Slow Food Movement, the concept of slowness has become a relevant and ethical topic that is often related to what is organic, local and sustainable. While the notion and impact of slowness have been studied in different areas such as food (Petrini 2003), media (Rauch 2011), medicine (Wear et al. 2015) and education (O’Neill 2014), museums are yet to be investigated in depth. Through the lens of Appraisal Theory (Martin, White 2005) and corpus linguistics (Sinclair 2004), I focus on a diachronic study of the language of evaluation adopted in the Slow Art Day official blog, which keeps a record of the reports of the museums that take part in the yearly event. By using both a quantitative and qualitative approach, I focus on how appraisal is used to enhance and promote the new and different semantic dimensions related to slowness. My analysis of the Slow Art Day blog will illustrate how slowness is no longer related to the semantic dimension of Time, but also to those of Wellbeing and Inclusiveness, while a close study of evaluative language will show how these dimensions are interconnected to one another

Cognitive architecture of multimodal multidimensional dialogue management

Numerous studies show that participants of real-life dialogues happen to get involved in rather dynamic non-sequential interactions. This challenges the dialogue system designs based on a reactive interlocutor paradigm and calls for dialog systems that can be characterised as a proactive learner, accomplished multitasking planner and adaptive decision maker. Addressing this call, the thesis brings innovative integration of cognitive models into the human-computer dialogue systems. This work utilises recent advances in Instance-Based Learning of Theory of Mind skills and the established Cognitive Task Analysis and ACT-R models. Cognitive Task Agents, producing detailed simulation of human learning, prediction, adaption and decision making, are integrated in the multi-agent Dialogue Man-ager. The manager operates on the multidimensional information state enriched with representations based on domain- and modality-specific semantics and performs context-driven dialogue acts interpretation and generation. The flexible technical framework for modular distributed dialogue system integration is designed and tested. The implemented multitasking Interactive Cognitive Tutor is evaluated as showing human-like proactive and adaptive behaviour in setting goals, choosing appropriate strategies and monitoring processes across contexts, and encouraging the user exhibit similar metacognitive competences