Papers by Pavel Danchenko

2 papers
Deploying a Retrieval based Response Model for Task Oriented Dialogues (2022.emnlp-industry)

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Challenge: a task-oriented dialogue system needs high conversational capability and can be easily adaptable to changing situations.
Approach: They propose a retrieval-based conversational model that can rank a large set of responses . they propose supervised training and fine-tuning on limited data collected through a human-in-the-loop platform .
Outcome: The proposed model can scale to rank a large set of responses in real-world situations.
Calibrating Imbalanced Classifiers with Focal Loss: An Empirical Study (2022.emnlp-industry)

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Challenge: Imbalanced data distributions can cause models to overfit to majority classes and output unreliable (mostly overconfident) predictions.
Approach: They propose to streamline the model development and deployment using focal loss to address imbalanced data distributions.
Outcome: The proposed model training with focal loss improves calibration and accuracy compared to standard cross-entropy loss.

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