Papers by Alessandro Pedrani
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. |
EPIC: Multi-Perspective Annotation of a Corpus of Irony (2023.acl-long)
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Simona Frenda, Alessandro Pedrani, Valerio Basile, Soda Marem Lo, Alessandra Teresa Cignarella, Raffaella Panizzon, Cristina Marco, Bianca Scarlini, Viviana Patti, Cristina Bosco, Davide Bernardi
| Challenge: | EPIC is the first annotated corpus for irony analysis based on data perspectivism . a recent trend in natural language processing (NLP) postulates that the disagreement among annotators in a language resource is a valuable source of knowledge, rather than noise that ought to be minimized or discarded. |
| Approach: | They propose to annotate an English perspectivist irony corpus based on data perspectivism . they validate the model by creating perspective-aware models that encode the perspectives of annotators grouped according to their demographic characteristics. |
| Outcome: | The proposed model can capture different perspectives on irony among different groups of annotators, and is more confident than non-perspectivist models. |
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. |