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

Developing a corpus of strategic conversation in The Settlers of Catan

International audienceWe describe a dialogue model and an implemented annotation scheme for a pilot corpus of annotated online chats concerning bargaining negotiations in the game The Settlers of Catan. We will use this model and data to analyze how conversations proceed in the absence of strong forms of cooperativity, where agents have diverging motives. Here we concentrate on the description of our annotation scheme for negotiation dialogues, illustrated with our pilot data, and some perspectives for future research on the issue

Inter-Coder Agreement for Computational Linguistics

This article is a survey of methods for measuring agreement among corpus annotators. It exposes the mathematics and underlying assumptions of agreement coefficients, covering Krippendorff's alpha as well as Scott's pi and Cohen's kappa; discusses the use of coefficients in several annotation tasks; and argues that weighted, alpha-like coefficients, traditionally less used than kappa-like measures in computational linguistics, may be more appropriate for many corpus annotation tasks—but that their use makes the interpretation of the value of the coefficient even harder. </jats:p

Data-based analysis of speech and gesture: the Bielefeld Speech and Gesture Alignment corpus (SaGA) and its applications

Lücking A, Bergmann K, Hahn F, Kopp S, Rieser H. Data-based analysis of speech and gesture: the Bielefeld Speech and Gesture Alignment corpus (SaGA) and its applications. Journal on Multimodal User Interfaces. 2013;7(1-2):5-18.Communicating face-to-face, interlocutors frequently produce multimodal meaning packages consisting of speech and accompanying gestures. We discuss a systematically annotated speech and gesture corpus consisting of 25 route-and-landmark-description dialogues, the Bielefeld Speech and Gesture Alignment corpus (SaGA), collected in experimental face-to-face settings. We first describe the primary and secondary data of the corpus and its reliability assessment. Then we go into some of the projects carried out using SaGA demonstrating the wide range of its usability: on the empirical side, there is work on gesture typology, individual and contextual parameters influencing gesture production and gestures’ functions for dialogue structure. Speech-gesture interfaces have been established extending unification-based grammars. In addition, the development of a computational model of speech-gesture alignment and its implementation constitutes a research line we focus on

Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues †

Research on the analysis of counselling conversations through natural language processing methods has seen remarkable growth in recent years. However, the potential of this field is still greatly limited by the lack of access to publicly available therapy dialogues, especially those with expert annotations, but it has been alleviated thanks to the recent release of AnnoMI, the first publicly and freely available conversation dataset of 133 faithfully transcribed and expert-annotated demonstrations of high- and low-quality motivational interviewing (MI)-an effective therapy strategy that evokes client motivation for positive change. In this work, we introduce new expert-annotated utterance attributes to AnnoMI and describe the entire data collection process in more detail, including dialogue source selection, transcription, annotation, and post-processing. Based on the expert annotations on key MI aspects, we carry out thorough analyses of AnnoMI with respect to counselling-related properties on the utterance, conversation, and corpus levels. Furthermore, we introduce utterance-level prediction tasks with potential real-world impacts and build baseline models. Finally, we examine the performance of the models on dialogues of different topics and probe the generalisability of the models to unseen topics

Interpersonal stance in police interviews: content analysis

A serious game for learning the social skills required for effective police interviewing is a challenging idea. Building artificial conversational characters that play the role of a suspect in a police interrogation game requires computational models of police interviews as well as of the internal psychological mechanisms that determine the behaviour of suspects in this special type of dialogues. Leary's interactional circumplex is used in police interview training as a theoretical framework to understand how suspects take stance during an interview and how this is related to the stance and the strategy that the interviewer takes. Interactional stance is a fuzzy notion. The question that we consider here is whether different observers of police nterviews agree on the type of stance that suspect and policemen take and express in a face-to-face interview. We analyzed police interviews and report about a stance annotation exercise. We conclude that although inter-annotator agreement on stance labeling on the level of speech segments is low, a majority voting meta-annotator" is able to reveal the important dynamics in stance taking in a police interview. Then we explore the relation between the stance taken by the suspect and turn-taking behaviour, overlaps, interruptions, pauses and silences. Our findings contribute to building computational models of non-player characters that allow more natural turn-taking behaviour in serious games instead of the one-at-a-time regime in interview training games