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In this paper, we compare two different approaches to language understanding for a human-robot interaction domain in which a human commander gives navigation instructions to a robot. We contrast a relevance-based classifier with a GPT-2 model, using about 2000 input-output examples as training data. With this level of training data, the relevance-based model outperforms the GPT-2 based model 79% to 8%. We also present a taxonomy of types of errors made by each model, indicating that they have somewhat different strengths and weaknesses, so we also examine the potential for a combined model.more » « less
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Marge, Matthew ; Espy-Wilson, Carol ; Ward, Nigel G. ; Alwan, Abeer ; Artzi, Yoav ; Bansal, Mohit ; Blankenship, Gil ; Chai, Joyce ; Daumé, Hal ; Dey, Debadeepta ; et al ( , Computer Speech & Language)null (Ed.)
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Chulaka Gunasekara, Seokhwan Kim ( , ArXivorg)null (Ed.)This paper introduces the Ninth Dialog System Technology Challenge (DSTC-9). This edition of the DSTC focuses on applying end-to-end dialog technologies for four distinct tasks in dialog systems, namely, 1. Task-oriented dialog Modeling with unstructured knowledge access, 2. Multi-domain task-oriented dialog, 3. Interactive evaluation of dialog, and 4. Situated interactive multi-modal dialog. This paper describes the task definition, provided datasets, baselines and evaluation set-up for each track. We also summarize the results of the submitted systems to highlight the overall trends of the state-of-the-art technologies for the tasks.more » « less