First Steps Towards the Automatic Construction of Argument-Diagrams from Real Discussions

This paper presents our efforts to create argument structures from meeting transcripts automatically. We show that unit labels of argument diagrams can be learnt and predicted by a computer with an accuracy of 78,52% and 51,43% on an unbalanced and balanced set respectively. We used a corpus of over 250 argument diagrams that was manually created by applying the Twente Argument Schema. In\ud this paper we also elaborate on this schema and we discuss applications and the role we foresee the diagrams to play

Query types in the meeting domain: assessing the role of argumentative structure in answering questions on meeting discussion records

We define a new task of question answering on meeting records and assess its difficulty in terms of types of information and retrieval techniques required. The importance of this task is revealed by the increasingly growing interest in the design of sophisticated interfaces for accessing meeting records such as meeting browsers. We ground our work on the empirical analysis of elicited user queries. We assess what is the type of information sought by the users and perform a user query classification along several semantic dimensions of the meeting content. We found that queries about argumentative processes and outcomes represent the majority among the elicited queries (about 60%). We also assess the difficulty in answering the queries and focus on the requirements of a prospective QA system to successfully deal with them. Our results suggest that standard Information Retrieval and Question Answering alone can only account for less than 20% of the queries and need to be completed with additional type of information and inference

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities

Answering questions about archived, annotated meetings

Retrieving information from archived meetings is a new domain of information retrieval that has received increasing attention in the past few years. Search in spontaneous spoken conversations has been recognized as more difficult than text-based document retrieval because meeting discussions contain two levels of information: the content itself, i.e. what topics are discussed, but also the argumentation process, i.e. what conflicts are resolved and what decisions are made. To capture the richness of information in meetings, current research focuses on recording meetings in Smart-Rooms, transcribing meeting discussion into text and annotating discussion with semantic higher-level structures to allow for efficient access to the data. However, it is not yet clear what type of user interface is best suited for searching and browsing such archived, annotated meetings. Content-based retrieval with keyword search is too naive and does not take into account the semantic annotations on the data. The objective of this thesis is to assess the feasibility and usefulness of a natural language interface to meeting archives that allows users to ask complex questions about meetings and retrieve episodes of meeting discussions based on semantic annotations. The particular issues that we address are: the need of argumentative annotation to answer questions about meetings; the linguistic and domain-specific natural language understanding techniques required to interpret such questions; and the use of visual overviews of meeting annotations to guide users in formulating questions. To meet the outlined objectives, we have annotated meetings with argumentative structure and built a prototype of a natural language understanding engine that interprets questions based on those annotations. Further, we have performed two sets of user experiments to study what questions users ask when faced with a natural language interface to annotated meeting archives. For this, we used a simulation method called Wizard of Oz, to enable users to express questions in their own terms without being influenced by limitations in speech recognition technology. Our experimental results show that technically it is feasible to annotate meetings and implement a deep-linguistic NLU engine for questions about meetings, but in practice users do not consistently take advantage of these features. Instead they often search for keywords in meetings. When visual overviews of the available annotations are provided, users refer to those annotations in their questions, but the complexity of questions remains simple. Users search with a breadth-first approach, asking questions in sequence instead of a single complex question. We conclude that natural language interfaces to meeting archives are useful, but that more experimental work is needed to find ways to incent users to take advantage of the expressive power of natural language when asking questions about meetings

Automatic annotation of context and speech acts for dialogue corpora

Richly annotated dialogue corpora are essential for new research directions in statistical learning approaches to dialogue management, context-sensitive interpretation, and context-sensitive speech recognition. In particular, large dialogue corpora annotated with contextual information and speech acts are urgently required. We explore how existing dialogue corpora (usually consisting of utterance transcriptions) can be automatically processed to yield new corpora where dialogue context and speech acts are accurately represented. We present a conceptual and computational framework for generating such corpora. As an example, we present and evaluate an automatic annotation system which builds ‘Information State Update' (ISU) representations of dialogue context for the Communicator (2000 and 2001) corpora of human-machine dialogues (2,331 dialogues). The purposes of this annotation are to generate corpora for reinforcement learning of dialogue policies, for building user simulations, for evaluating different dialogue strategies against a baseline, and for training models for context-dependent interpretation and speech recognition. The automatic annotation system parses system and user utterances into speech acts and builds up sequences of dialogue context representations using an ISU dialogue manager. We present the architecture of the automatic annotation system and a detailed example to illustrate how the system components interact to produce the annotations. We also evaluate the annotations, with respect to the task completion metrics of the original corpus and in comparison to hand-annotated data and annotations produced by a baseline automatic system. The automatic annotations perform well and largely outperform the baseline automatic annotations in all measures. The resulting annotated corpus has been used to train high-quality user simulations and to learn successful dialogue strategies. The final corpus will be made publicly availabl

Move, hold and touch: A framework for Tangible gesture interactive systems

© 2015 by the authors. Technology is spreading in our everyday world, and digital interaction beyond the screen, with real objects, allows taking advantage of our natural manipulative and communicative skills. Tangible gesture interaction takes advantage of these skills by bridging two popular domains in Human-Computer Interaction, tangible interaction and gestural interaction. In this paper, we present the Tangible Gesture Interaction Framework (TGIF) for classifying and guiding works in this field. We propose a classification of gestures according to three relationships with objects: move, hold and touch. Following this classification, we analyzed previous work in the literature to obtain guidelines and common practices for designing and building new tangible gesture interactive systems. We describe four interactive systems as application examples of the TGIF guidelines and we discuss the descriptive, evaluative and generative power of TGIF

Mensch-Maschine-Systeme : Wissenschaftliches Kolloquium 5. März 2009, Fraunhofer IITB

Das informationstechnische Kolloquium Mensch-Maschine-Systeme des Fraunhofer IITB gibt anlässlich des 80sten Geburtstags seines ehemaligen Leiters und Präsidenten der Fraunhofer-Gesellschaft, Prof. Dr. Max Syrbe, einen Überblick über Geschichte, Stand und Trends der angewandten Forschung auf dem Gebiet der Mensch-Maschine-Systeme, welcher sich von der Ethik der Techniknutzung über die Automatisierungssysteme bis zu dem Maschinensehen für die Mensch-Maschine-Interaktion erstreckt