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The Need for Real-Time Crowd Generation of Task Lists from Speech

Crowdsourcing has made robust speech-based interactive systems possible (Lasecki et al. 2012; 2014; 2015). Speechbased interaction provides an effective and immediate way for workers to understand requester intent via streaming audio (Lasecki et al. 2015; Lee et al. 2017), or in near realtime via audio recordings (Bigham et al. 2010; Nebeling et al. 2016; Chen et al. 2017; Zhong et al. 2015). Speech is a powerful way to describe requests because of the flexibility and context that can be easily provided (e.g., Can you recommend a birthday present for a five year old boy? I don’t know what kids like these days. I don’t want to spend more than $30. It’s for my nephew.). However, since the verbal description of a complex request can get lengthy, crowd workers may need to revisit it (given a recording), or ask the requester to repeat the description (given streaming audio). To that end, it would be beneficial for crowd workers to organize and archive complex requests for later navigation and retrieval information. In this paper, we introduce Speech-To-Tasks, a tool that takes audio input and generates a hierarchical list of sub-tasks. Our Speech-To-Tasks system leverages crowds to segment audio into multiple parts and presents them hierarchically so that a complex task can be decomposed into a set of subtasks. The generated list of items can be used for crowd workers to self-coordinate, to avoid conflicts, as well as for requesters to monitor their progress. Further, converting speech into a structured summary itself can be useful in various settings (e.g., taking notes in meetings, summarizing a lecture, generating to-do items from a verbal request). We aim to provide a general solution for speechbased tasks, especially for near/real-time applications in crowdsourcing (Lasecki et al. 2011). In addition, findings from this work can help inform the design of intelligent conversational assistants like Apple Siri or Amazon Echo, and improve such systems to resolve more complex tasks. In this paper, we outline the motivation and challenges in making complex verbal requests, and introduce our ongoing work with the following target contributions: