Papers by Joseph J. Peper

2 papers
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction (D19-1)

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Challenge: Task-oriented dialog systems need to know when a query falls outside their range of supported intents.
Approach: They propose a dataset that includes queries that are out-of-scope and 150 intent classes over 10 domains.
Outcome: The proposed dataset includes queries that are out-of-scope, i.e., queries that do not fall into any of the system’s supported intents.
A Large-Scale Corpus for Conversation Disentanglement (P19-1)

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Challenge: a dataset of 77,563 messages manually annotated with reply-structure graphs disentangles conversations and defines internal conversation structure.
Approach: They use a dataset of 77,563 messages manually annotated with reply-structure graphs to disentangle conversations and define internal conversation structure.
Outcome: The new dataset is 16 times larger than all previous datasets combined and includes adjudication of annotation disagreements and context.

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