Papers by Alessandro Manzotti
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. |
Semantic Diversity for Natural Language Understanding Evaluation in Dialog Systems (2020.coling-industry)
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| Challenge: | a dialog system is used to evaluate NLU models using aggregated metrics on a large number of utterances. |
| Approach: | They propose a method to generate a test set with high semantic diversity for NLU evaluation in dialog systems. |
| Outcome: | The proposed test sets are based on high diversity of utterances from different regions of the utteration embedding space. |