Patrick Ernst


2022

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Calibrating Imbalanced Classifiers with Focal Loss: An Empirical Study
Cheng Wang | Jorge Balazs | György Szarvas | Patrick Ernst | Lahari Poddar | Pavel Danchenko
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

Imbalanced data distribution is a practical and common challenge in building production-level machine learning (ML) models in industry, where data usually exhibits long-tail distributions. For instance, in virtual AI Assistants, such as Google Assistant, Amazon Alexa and Apple Siri, the “play music” or “set timer” utterance is exposed to an order of magnitude more traffic than other skills. This can easily cause trained models to overfit to the majority classes, categories or intents, lead to model miscalibration. The uncalibrated models output unreliable (mostly overconfident) predictions, which are at high risk of affecting downstream decision-making systems. In this work, we study the calibration of production models in the industry use-case of predicting product return reason codes in customer service conversations of an online retail store; The returns reasons also exhibit class imbalance.To alleviate the resulting miscalibration in the production ML model, we streamline the model development and deployment using focal loss (CITATION).We empirically show the effectiveness of model training with focal loss in learning better calibrated models, as compared to standard cross-entropy loss. Better calibration, in turn, enables better control of the precision-recall trade-off for the models deployed in production.

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Deploying a Retrieval based Response Model for Task Oriented Dialogues
Lahari Poddar | György Szarvas | Cheng Wang | Jorge Balazs | Pavel Danchenko | Patrick Ernst
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

Task-oriented dialogue systems in industry settings need to have high conversational capability, be easily adaptable to changing situations and conform to business constraints. This paper describes a 3-step procedure to develop a conversational model that satisfies these criteria and can efficiently scale to rank a large set of response candidates. First, we provide a simple algorithm to semi-automatically create a high-coverage template set from historic conversations without any annotation. Second, we propose a neural architecture that encodes the dialogue context and applicable business constraints as profile features for ranking the next turn. Third, we describe a two-stage learning strategy with self-supervised training, followed by supervised fine-tuning on limited data collected through a human-in-the-loop platform. Finally, we describe offline experiments and present results of deploying our model with human-in-the-loop to converse with live customers online.

2016

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DeepLife: An Entity-aware Search, Analytics and Exploration Platform for Health and Life Sciences
Patrick Ernst | Amy Siu | Dragan Milchevski | Johannes Hoffart | Gerhard Weikum
Proceedings of ACL-2016 System Demonstrations

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Disambiguation of entities in MEDLINE abstracts by combining MeSH terms with knowledge
Amy Siu | Patrick Ernst | Gerhard Weikum
Proceedings of the 15th Workshop on Biomedical Natural Language Processing