Papers by Davide Bernardi
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. |
PERSEVAL: A Framework for Perspectivist Classification Evaluation (2025.emnlp-main)
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Soda Marem Lo, Silvia Casola, Erhan Sezerer, Valerio Basile, Franco Sansonetti, Antonio Uva, Davide Bernardi
| Challenge: | Perspectivist evaluation practices in NLP remain fragmented and inconsistent . |
| Approach: | They propose a framework that evaluates perspectivist models at the individual annotator level and treats annotators and users as distinct entities, consistent with real-world scenarios. |
| Outcome: | The proposed framework evaluates annotators and users as distinct entities consistent with real-world scenarios. |
Learning to Ask Informative Questions: Enhancing LLMs with Preference Optimization and Expected Information Gain (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) often perform poorly in generating informative questions, as measured by expected information gain (EIG). |
| Approach: | They propose to use a large language model to enhance the informativeness of LLM-generated questions in 20-question game dialogues by applying a Direct Preference Optimization algorithm to generate low-EIG and high-EI questions. |
| Outcome: | The proposed method produces more effective questions even in domains different from those used to train the DPO model. |
When Speed Meets Intelligence: Scalable Conversational NER in an Ever-evolving World (2026.eacl-industry)
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| Challenge: | Large Language Models excel at understanding conversational semantics, but lack of data makes them impractical for production deployment. |
| Approach: | They propose a pipeline for generating multilingual conversational NER datasets with minimal human validation and a framework that leverages LLMs as semantic filters combined with catalog-based entity grounding to label live traffic data. |
| Outcome: | The proposed framework outperforms existing models on public and private conversations by 97.12% on CoNLL-2003 and 83.09% on OntoNotes 5.0. |
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. |
All-in-one: Understanding and Generation in Multimodal Reasoning with the MAIA Benchmark (2025.findings-emnlp)
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Davide Testa, Giovanni Bonetta, Raffaella Bernardi, Alessandro Bondielli, Alessandro Lenci, Alessio Miaschi, Lucia Passaro, Bernardo Magnini
| Challenge: | MAIA evaluates visual language models on video-related tasks using reasoning categories that aim to disentangle language and vision relations. |
| Approach: | a native-italian benchmark is designed for fine-grained investigation of the reasoning abilities of visual language models on videos. |
| Outcome: | The benchmark evaluates visual language models on two aligned tasks and a visual question-answering task. |
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. |
Playpen: An Environment for Exploring Learning From Dialogue Game Feedback (2025.emnlp-main)
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Nicola Horst, Davide Mazzaccara, Antonia Schmidt, Michael Sullivan, Filippo Momentè, Luca Franceschetti, Philipp Sadler, Sherzod Hakimov, Alberto Testoni, Raffaella Bernardi, Raquel Fernández, Alexander Koller, Oliver Lemon, David Schlangen, Mario Giulianelli, Alessandro Suglia
| Challenge: | In this paper, we investigate whether Dialogue Games—goal-directed and rule-governed activities driven predominantly by verbal actions—can also serve as a source of feedback signals for learning. |
| Approach: | They introduce Playpen, an environment for off- and online learning through Dialogue Game self-play, and investigate a representative set of post-training methods: supervised fine-tuning, direct alignment and reinforcement learning with Group Relative Policy Optimization. |
| Outcome: | The proposed model improves performance on unseen instances, but negatively impacts other skills, while interactive learning shows balanced improvements without loss of skills. |