Papers by Motoki Sano

1 papers
PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs (2023.emnlp-main)

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Challenge: PRESTO dataset contains 550K contextual multilingual conversations between humans and virtual assistants.
Approach: They propose to use a dataset of 550K contextual multilingual conversations between humans and virtual assistants to study some of the more challenging aspects of parsing realistic conversations.
Outcome: The dataset contains 550K contextual conversations between humans and virtual assistants.

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