Abstract Meaning Representation for Human-Robot Dialogue

In this research, we begin to tackle the challenge of natural language understanding (NLU) in the context of the development of a robot dialogue system. We explore the adequacy of Abstract Meaning Representation (AMR) as a conduit for NLU. First, we consider the feasibility of using existing AMR parsers for automatically creating meaning representations for robot-directed transcribed speech data. We evaluate the quality of output of two parsers on this data against a manually annotated gold-standard data set. Second, we evaluate the semantic coverage and distinctions made in AMR overall: how well does it capture the meaning and distinctions needed in our collaborative human-robot dialogue domain? We find that AMR has gaps that align with linguistic information critical for effective human-robot collaboration in search and navigation tasks, and we present task-specific modifications to AMR to address the deficiencies

Group Emotions in Collective Reasoning: a Model

International audienceEducation and cognition research today generally recognize the tri-dimensional nature of reasoning processes as involving cognitive, social and emotional phenomena. However, there is so far no theoretical framework articulating these three dimensions from a descriptive perspective. This paper aims at presenting a first model of how group emotions work in collective reasoning, and specifies their social and cognitive functions. This model is inspired both from a multidisciplinary literature review and our extensive previous empirical work on an international corpus of videotaped student debates. The cognitive function of emotions is defined in reference to the process of schematization (Grize 1996, 1997) and associated emotional framing (Polo et al. 2013). On the other hand, the social function of emotions refers to recognition-oriented behaviors that correspond to engagement into specific types of group talk (e.g. Mercer 1996), implying specific politeness rules or face-preservation systems (Brown and Levinson 1988). We believe that our multi-dimensional and multi-level approach to group reasoning, which mostly employs a linguistic perspective, can be applied to a diversity of contexts. We hope it will serve as a basis for further discussion on the role of emotions in reasoning among the interdisciplinary community of argumentation studies

A principlist-based study of the ethical design and acceptability of artificial social agents

Artificial Social Agents (ASAs), which are AI software driven entities programmed with rules and preferences to act autonomously and socially with humans, are increasingly playing roles in society. As their sophistication grows, humans will share greater amounts of personal information, thoughts, and feelings with ASAs, which has significant ethical implications. We conducted a study to investigate what ethical principles are of relative importance when people engage with ASAs and whether there is a relationship between people’s values and the ethical principles they prioritise. The study uses scenarios, embedded with the five AI4People ethical principles (Beneficence, Non-maleficence, Autonomy, Justice, and Explicability), involving ASAs taking on roles traditionally played by humans to understand whether these roles and behaviours (including dialogues) are seen as acceptable or unacceptable. Results from 268 participants reveal the greatest sensitivity to ASA behaviours that relate to Autonomy, Justice, Explicability, and the privacy of their personal data. Models were created using Schwartz’s Refined Values as a possible indicator of how stakeholders discern and prioritise the different AI4People ethical principles when interacting with ASAs. Our findings raise issues around the ethical acceptability of ASAs for nudging and changing behaviour due to participants’ desire for autonomy and their concerns over deceptive ASA behaviours such as pretending to have memories and emotions

A Computational Model of Non-Cooperation in Natural Language Dialogue

A common assumption in the study of conversation is that participants fully cooperate in order to maximise the effectiveness of the exchange and ensure communication flow. This assumption persists even in situations in which the private goals of the participants are at odds: they may act strategically pursuing their agendas, but will still adhere to a number of linguistic norms or conventions which are implicitly accepted by a community of language users. However, in naturally occurring dialogue participants often depart from such norms, for instance, by asking inappropriate questions, by avoiding to provide adequate answers or by volunteering information that is not relevant to the conversation. These are examples of what we call linguistic non-cooperation. This thesis presents a systematic investigation of linguistic non-cooperation in dialogue. Given a specific activity, in a specific cultural context and time, the method proceeds by making explicit which linguistic behaviours are appropriate. This results in a set of rules: the global dialogue game. Non-cooperation is then measured as instances in which the actions of the participants are not in accordance with these rules. The dialogue game is formally defined in terms of discourse obligations. These are actions that participants are expected to perform at a given point in the dialogue based on the dialogue history. In this context, non-cooperation amounts to participants failing to act according to their obligations. We propose a general definition of linguistic non-cooperation and give a specific instance for political interview dialogues. Based on the latter, we present an empirical method which involves a coding scheme for the manual annotation of interview transcripts. The degree to which each participant cooperates is automatically determined by contrasting the annotated transcripts with the rules in the dialogue game for political interviews. The approach is evaluated on a corpus of broadcast political interviews and tested for correlation with human judgement on the same corpus. Further, we describe a model of conversational agents that incorporates the concepts and mechanisms above as part of their dialogue manager. This allows for the generation of conversations in which the agents exhibit varying degrees of cooperation by controlling how often they favour their private goals instead of discharging their discourse obligations

Design of a horizontal cooperation architecture based on metamodels, focused on electronic commerce platforms for mobile devices: case study of smes in the textile sector in Bogotá

Este artículo evidencia una arquitectura de cooperación horizontal para facilitar el proceso de transporte de mercancía en plataformas de comercio electrónico. Para ello se generó una taxonomía, un modelo semántico, una ontología que permita modelar dicha logística electrónica, después se utilizará la ontología generada para crear un metamodelo y luego un DSL basado en este, finalmente con MDE y aplicando transformación de los datos por medio de QVT, se logrará un modelo de datos interoperable para varias plataformas de comercio electrónico, en el que los operadores logísticos puedan ver la mercancía de forma transversal.The main objective of this thesis is to propose horizontal cooperation architecture to facilitate the shipping process in e-commerce platforms. First we will generate a taxonomy, a semantic model, an ontology that allows simulate that e-logistics, use the ontology to create a Metamodel and later an DSL, finally with MDE and applying data transformation using QVT, it will achieve a model of interoperable data e-commerce platforms, and the logistics operators will see the comodity transversely

Academic self-concept at post-16: Comparing peer-guided, dyadic and autonomous learning as transitional interventions

Transitioning from GCSE to ‘A’ level, students struggle emotionally and academically to meet the requirements of ‘A’ level study, drop out and fail (Hall, 2003; DfES, 2011a). The OECD (2003) found that post-16 learners rarely know how to learn on their own whereas effective learners have a positive academic self-concept related to higher attainment (Marsh, 2007). This study followed transitioning students working either collaboratively or alone asking what happens when a transitional intervention is used, such as a collaborative learning strategy, with students studying psychology and ethics for the first time and is there any impact on their academic self-concept and attainment? Rooted in a social constructivist paradigm, a mixed method, 9-month study followed 73 learners in their first 12 weeks of an ‘A’ level programme. Students chose one of three groups; a group guided by a more knowledgeable peer, dyadic pairs or alone. A concurrent triangulation strategy was employed to quantitatively and qualitatively assess students’ transitional experiences. Qualitative data revealed students valued a collaborative strategy. They felt a significant emotional attachment to their peers, which aided academic confidence and understanding. Dovetailed with quantitative data all three contexts showed increased academic self-concept correlated positively with increase in ALIS expected grades (r= +0.299). Emerging themes were the importance of choice of study group, the need for fun, that collaboration stabilised students’ emotional wellbeing, students developed a positive regard for others, an increased positive social identity and improved academic self-concept. Findings illustrate schools can facilitate students’ transition, protect them from isolation, boost their emotional wellbeing, and support their academic confidence, not only increasing their academic attainment but preparing them for life-long learning. This research is not only of value to students but also to teachers, headteachers and governors as well as academics and leaders of further education who lobby for more resilient, competent and buoyant learners

Références

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