Papers by Jorge Balazs
Gating Mechanisms for Combining Character and Word-level Word Representations: an Empirical Study (N19-3)
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| Challenge: | Existing studies show that combining character and word-level representations improves word and sentence representations . however, word-based embeddings do not account for derivational processes resulting in syntactically-similar words with different meanings. |
| Approach: | They propose to combine character and word-level representations to improve word and sentence representations. |
| Outcome: | The proposed method performed well in several word similarity datasets. |
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. |