What is a wizard of oz setup?
a setup where the seeker interacts with a real conversational interface and the wizard, an intermediary, performs actions related to the seeker's message
Conversational information seeking (CIS) has been recognized as a major emerging research area in information retrieval. Such research will require data and tools, to allow the implementation and study of conversational systems. This paper introduces Macaw, an open-source framework with a modular architecture for CIS research. Macaw supports multi-turn, multi-modal, and mixed-initiative interactions, and enables research for tasks such as document retrieval, question answering, recommendation, and structured data exploration. It has a modular design to encourage the study of new CIS algorithms, which can be evaluated in batch mode. It can also integrate with a user interface, which allows user studies and data collection in an interactive mode, where the back end can be fully algorithmic or a wizard of oz setup. Macaw is distributed under the MIT License.
The rapid growth in speech and small screen interfaces, particularly on mobile devices, has significantly influenced the way users interact with intelligent systems to satisfy their information needs. The growing interest in personal digital assistants, such as Amazon Alexa, Apple Siri, Google Assistant, and Microsoft Cortana, demonstrates the willingness of users to employ conversational interactions BIBREF0. As a result, conversational information seeking (CIS) has been recognized as a major emerging research area in the Third Strategic Workshop on Information Retrieval (SWIRL 2018) BIBREF1. Research progress in CIS relies on the availability of resources to the community. There have been recent efforts on providing data for various CIS tasks, such as the TREC 2019 Conversational Assistance Track (CAsT), MISC BIBREF2, Qulac BIBREF3, CoQA BIBREF4, QuAC BIBREF5, SCS BIBREF6, and CCPE-M BIBREF7. In addition, BIBREF8 have implemented a demonstration for conversational movie recommendation based on Google's DialogFlow. Despite all of these resources, the community still feels the lack of a suitable platform for developing CIS systems. We believe that providing such platform will speed up the progress in conversational information seeking research. Therefore, we developed a general framework for supporting CIS research. The framework is called Macaw. This paper describes the high-level architecture of Macaw, the supported functionality, and our future vision. Researchers working on various CIS tasks should be able to take advantage of Macaw in their projects. Macaw is designed based on a modular architecture to support different information seeking tasks, including conversational search, conversational question answering, conversational recommendation, and conversational natural language interface to structured and semi-structured data. Each interaction in Macaw (from both user and system) is a Message object, thus a conversation is a list of Messages. Macaw consists of multiple actions, each action is a module that can satisfy the information needs of users for some requests. For example, search and question answering can be two actions in Macaw. Even multiple search algorithms can be also seen as multiple actions. Each action can produce multiple outputs (e.g., multiple retrieved documents). For every user interaction, Macaw runs all actions in parallel. The actions' outputs produced within a predefined time interval (i.e., an interaction timeout constant) are then post-processed. Macaw can choose one or combine multiple of these outputs and prepare an output Message object as the user's response. The modular design of Macaw makes it relatively easy to configure a different user interface or add a new one. The current implementation of Macaw supports a command line interface as well as mobile, desktop, and web apps. In more detail, Macaw's interface can be a Telegram bot, which supports a wide range of devices and operating systems (see FIGREF4). This allows Macaw to support multi-modal interactions, such as text, speech, image, click, etc. A number of APIs for automatic speech recognition and generation have been employed to support speech interactions. Note that the Macaw's architecture and implementation allows mixed-initiative interactions. The research community can benefit from Macaw for the following purposes: [leftmargin=*] Developing algorithms, tools, and techniques for CIS. Studying user interactions with CIS systems. Performing CIS studies based on an intermediary person and wizard of oz. Preparing quick demonstration for a developed CIS model.