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Creators/Authors contains: "Rumshisky, A"

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  1. ; ; ; ; ; (, ELRA and ICCL)
    For the past decade, temporal annotation has been sparse: only a small portion of event pairs in a text was annotated. We present NarrativeTime, the first timeline-based annotation framework that achieves full coverage of all possible TLINKs. To compare with the previous SOTA in dense temporal annotation, we perform full re-annotation of the classic TimeBankDense corpus (American English), which shows comparable agreement with a signigicant increase in density. We contribute TimeBankNT corpus (with each text fully annotated by two expert annotators), extensive annotation guidelines, open-source tools for annotation and conversion to TimeML format, and baseline results. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; (, Proceedings of the 2022 American Society for Engineering Education Annual Conference & Exposition)
    Accepted Manuscript Full Text Available
  3. ; ; ; (, American Society for Engineering Education)
  4. ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    The recent explosion in question answering research produced a wealth of both factoid reading comprehension (RC) and commonsense reasoning datasets. Combining them presents a different kind of task: deciding not simply whether information is present in the text, but also whether a confident guess could be made for the missing information. We present QuAIL, the first RC dataset to combine text-based, world knowledge and unanswerable questions, and to provide question type annotation that would enable diagnostics of the reasoning strategies by a given QA system. QuAIL contains 15K multi-choice questions for 800 texts in 4 domains. Crucially, it offers both general and text-specific questions, unlikely to be found in pretraining data. We show that QuAIL poses substantial challenges to the current state-of-the-art systems, with a 30% drop in accuracy compared to the most similar existing dataset. 
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    Full Text Available