Papers by Michael A. Laurenzano
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction (D19-1)
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Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, Jason Mars
| 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. |
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems (N19-1)
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Stefan Larson, Anish Mahendran, Andrew Lee, Jonathan K. Kummerfeld, Parker Hill, Michael A. Laurenzano, Johann Hauswald, Lingjia Tang, Jason Mars
| Challenge: | Existing methods to detect outliers in text have been neglected in NLP . outlier detection is a problem in dialog systems where text is often no more than a few sentences in length. |
| Approach: | They propose a technique that uses sentence embeddings to detect outliers in short texts using neural sentence embeds and distance-based outlier detection. |
| Outcome: | The proposed technique detects outliers in a corpus of short texts while generating highly diverse corpora that produce more robust intent classification and slot-filling models. |