Papers by Anish Mahendran
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. |
Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods (2020.coling-main)
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| Challenge: | Standard slot-filling models train or finetune on large datasets of carefully-annotated data that is domain specific. |
| Approach: | They propose automatic methods to identify inconsistencies in crowd-annotated data . a slot-filling model can extract the tokens "New York" as a TO LOCATION slot in a query . |
| Outcome: | The proposed methods reveal inconsistencies in data, though there is scope for improvement. |
Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness (2020.emnlp-main)
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Stefan Larson, Anthony Zheng, Anish Mahendran, Rishi Tekriwal, Adrian Cheung, Eric Guldan, Kevin Leach, Jonathan K. Kummerfeld
| Challenge: | Recent work on dialog has found that crowdsourced data can have limited diversity as workers tend to write simple variations from prompts. |
| Approach: | They propose a general approach for guiding workers to write more diverse text by iteratively constraining their writing. |
| Outcome: | The proposed approach improves performance on dialog tasks and improves on existing datasets. |
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. |