Understanding and Answering Incomplete Questions

Angus Addlesee, Marco Damonte

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


Voice assistants interrupt people when they pause mid-question, a frustrating interaction that requires the full repetition of the entire question again. This impacts all users, but particularly people with cognitive impairments. In human-human conversation, these situations are recovered naturally as people understand the words that were uttered. In this paper we build answer pipelines which parse incomplete questions and repair them following human recovery strategies. We evaluated these pipelines on our new corpus, SLUICE. It contains 21,000 interrupted questions, from LC-QuAD 2.0 and QALD-9-plus, paired with their underspecified SPARQL queries. Compared to a system that is given the full question, our best partial understanding pipeline answered only 0.77% fewer questions. Results show that our pipeline correctly identifies what information is required to provide an answer but is not yet provided by the incomplete question. It also accurately identifies where that missing information belongs in the semantic structure of the question.
Original languageEnglish
Title of host publicationCUI '23: Proceedings of the 5th International Conference on Conversational User Interfaces
PublisherAssociation for Computing Machinery
ISBN (Print)9798400700149
Publication statusPublished - 19 Jul 2023
Event5th International Conference on Conversational User Interfaces 2023 - Eindhoven, Netherlands
Duration: 19 Jul 202321 Jul 2023


Conference5th International Conference on Conversational User Interfaces 2023


  • accessibility
  • human agent interaction
  • knowledge base question answering
  • semantic parsing
  • voice user experience

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software


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