Papers by Andrea Caciolai
Regression-Free Model Updates for Spoken Language Understanding (2023.acl-industry)
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| Challenge: | Recent work has proposed methods for minimizing regressions caused by model updates . focus is on spoken language understanding models, which are unexplored . |
| Approach: | They propose a focal distillation technique to reduce regressions in goal-oriented dialog systems . they also evaluate its effectiveness for key language understanding tasks . |
| Outcome: | The proposed technique outperforms naive supervised training in mislabeled data and label expansion settings. |
Mitigating the Burden of Redundant Datasets via Batch-Wise Unique Samples and Frequency-Aware Losses (2023.acl-industry)
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Donato Crisostomi, Andrea Caciolai, Alessandro Pedrani, Kay Rottmann, Alessandro Manzotti, Enrico Palumbo, Davide Bernardi
| Challenge: | Existing solutions to train deep learning models on redundant datasets are difficult to implement in industrial settings. |
| Approach: | They propose a method to eliminate duplicates at the batch level without altering the data distribution observed by the model. |
| Outcome: | The proposed approach reduces training times on models on redundant datasets by up to 87% and 46% on average, with a drop in model performance of 0.2% relative at worst. |