Anish Mahendran


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Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods
Stefan Larson | Adrian Cheung | Anish Mahendran | Kevin Leach | Jonathan K. Kummerfeld
Proceedings of the 28th International Conference on Computational Linguistics

Slot-filling models in task-driven dialog systems rely on carefully annotated training data. However, annotations by crowd workers are often inconsistent or contain errors. Simple solutions like manually checking annotations or having multiple workers label each sample are expensive and waste effort on samples that are correct. If we can identify inconsistencies, we can focus effort where it is needed. Toward this end, we define six inconsistency types in slot-filling annotations. Using three new noisy crowd-annotated datasets, we show that a wide range of inconsistencies occur and can impact system performance if not addressed. We then introduce automatic methods of identifying inconsistencies. Experiments on our new datasets show that these methods effectively reveal inconsistencies in data, though there is further scope for improvement.

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Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness
Stefan Larson | Anthony Zheng | Anish Mahendran | Rishi Tekriwal | Adrian Cheung | Eric Guldan | Kevin Leach | Jonathan K. Kummerfeld
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Diverse data is crucial for training robust models, but crowdsourced text often lacks diversity as workers tend to write simple variations from prompts. We propose a general approach for guiding workers to write more diverse text by iteratively constraining their writing. We show how prior workflows are special cases of our approach, and present a way to apply the approach to dialog tasks such as intent classification and slot-filling. Using our method, we create more challenging versions of test sets from prior dialog datasets and find dramatic performance drops for standard models. Finally, we show that our approach is complementary to recent work on improving data diversity, and training on data collected with our approach leads to more robust models.


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An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
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
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope—i.e., queries that do not fall into any of the system’s supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. We evaluate a range of benchmark classifiers on our dataset along with several different out-of-scope identification schemes. We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries. Our dataset and evaluation fill an important gap in the field, offering a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.

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Outlier Detection for Improved Data Quality and Diversity in Dialog Systems
Stefan Larson | Anish Mahendran | Andrew Lee | Jonathan K. Kummerfeld | Parker Hill | Michael A. Laurenzano | Johann Hauswald | Lingjia Tang | Jason Mars
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

In a corpus of data, outliers are either errors: mistakes in the data that are counterproductive, or are unique: informative samples that improve model robustness. Identifying outliers can lead to better datasets by (1) removing noise in datasets and (2) guiding collection of additional data to fill gaps. However, the problem of detecting both outlier types has received relatively little attention in NLP, particularly for dialog systems. We introduce a simple and effective technique for detecting both erroneous and unique samples in a corpus of short texts using neural sentence embeddings combined with distance-based outlier detection. We also present a novel data collection pipeline built atop our detection technique to automatically and iteratively mine unique data samples while discarding erroneous samples. Experiments show that our outlier detection technique is effective at finding errors while our data collection pipeline yields highly diverse corpora that in turn produce more robust intent classification and slot-filling models.