Conversational strategies : impact on search performance in a goal-oriented task

Conversational search relies on an interactive, natural language exchange between a user, who has an information need, and a search system, which elicits and reveals information. Prior research posits that due to the non-persistent nature of speech, conversational agents (CAs) should support users in their search task by: (1) actively suggesting query reformulations, and (2) providing summaries of the available options. Currently, however, the majority of CAs are passive (i.e. lack interaction initiative) and respond by providing lists of results – consequently putting more cognitive strain on users. To investigate the potential benefit of active search support and summarising search results, we performed a lab-based user study, where twenty-four participants undertook four goal-oriented search tasks (booking a flight). A 2x2 within subjects design was used where the CAs strategies varied with respect to elicitation (Passive vs Active) and revealment (Listing vs. Summarising). Results show that when the CA’s elicitation was Active, participant’s task performance improved significantly. Confirming speculations that Active elicitation can lead to improved outcomes for end-users. A similar trend, though to the lesser extent, was observed for revealment – where Summarising results led to better performance than Listing them. These findings are the beginning of, but also highlight the need for, research into design and evaluation of conversational strategies that active or pro-active CAs should employ to support better search performance

TaskMAD: a Platform for Multimodal Task-Centric Knowledge-Grounded Conversational Experimentation

The role of conversational assistants continues to evolve, beyond simple voice commands to ones that support rich and complex tasks in the home, car, and even virtual reality. Going beyond simple voice command and control requires agents and datasets blending structured dialogue, information seeking, grounded reasoning, and contextual question-answering in a multimodal environment with rich image and video content. In this demo, we introduce Task-oriented Multimodal Agent Dialogue (TaskMAD), a new platform that supports the creation of interactive multimodal and task-centric datasets in a Wizard-of-Oz experimental setup. TaskMAD includes support for text and voice, federated retrieval from text and knowledge bases, and structured logging of interactions for offline labeling. Its architecture supports a spectrum of tasks that span open-domain exploratory search to traditional frame-based dialogue tasks. It's open-source and offers rich capability as a platform used to collect data for the Amazon Alexa Prize Taskbot challenge, TREC Conversational Assistance track, undergraduate student research, and others. TaskMAD is distributed under the MIT license

Social talk capabilities for dialogue systems

Small talk capabilities are an important but very challenging extension to dialogue systems. Small talk (or social talk) refers to a kind of conversation, which does not focus on the exchange of information, but on the negotiation of social roles and situations. The goal of this thesis is to provide knowledge, processes and structures that can be used by dialogue systems to satisfactorily participate in social conversations. For this purpose the thesis presents research in the areas of natural-language understanding, dialogue management and error handling. Nine new models of social talk based on a data analysis of small talk conversations are described. The functionally-motivated and content-abstract models can be used for small talk conversations on various topics. The basic elements of the models consist of dialogue acts for social talk newly developed on basis of social science theory. The thesis also presents some conversation strategies for the treatment of so-called out-of-domain (OoD) utterances that can be used to avoid errors in the input understanding of dialogue systems. Additionally, the thesis describes a new extension to dialogue management that flexibly manages interwoven dialogue threads. The small talk models as well as the strategies for handling OoD utterances are encoded as computational dialogue threads

Pro-active Meeting Assistants : Attention Please!

This paper gives an overview of pro-active meeting assistants, what they are and when they can be useful. We explain how to develop such assistants with respect to requirement definitions and elaborate on a set of Wizard of Oz experiments, aiming to find out in which form a meeting assistant should operate to be accepted by participants and whether the meeting effectiveness and efficiency can be improved by an assistant at all