Papers by György Szarvas
The Multilingual Amazon Reviews Corpus (2020.emnlp-main)
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| Challenge: | The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019 . |
| Approach: | They propose to use mean absolute error (MAE) instead of classification accuracy for this task since MAE accounts for ordinal nature of the ratings. |
| Outcome: | The proposed model uses mean absolute error (MAE) instead of classification accuracy since MAE accounts for ordinal nature of the ratings. |
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. |
Learning from Relevant Subgoals in Successful Dialogs using Iterative Training for Task-oriented Dialog Systems (2024.findings-emnlp)
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| Challenge: | Task-oriented Dialog (ToD) systems have to solve multiple subgoals to accomplish user goals, whereas feedback is often obtained only at the end of the dialog. |
| Approach: | They propose an iterative training approach that uses subgoals to improve task-oriented dialog systems. |
| Outcome: | The proposed approach improves on a popular ToD benchmark by combining fine-tuning and preference learning steps. |
Few Shot Rationale Generation using Self-Training with Dual Teachers (2023.findings-acl)
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| Challenge: | Existing models that generate free-text explanations for annotated labels are expensive and require a large annotation dataset. |
| Approach: | They propose a self-training approach leveraging both labeled and unlabeled data to further improve few-shot models by combining teacher models and a multi-tasking student model. |
| Outcome: | The proposed model improves on three public datasets and can generate a free-text explanation for predicted labels. |
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. |