4 citations · 4 across the 8 of their papers we have counts for
8 papers
Multilingual Training and Evaluation Resources for Vision-Language Models
Daniela Baiamonte, Elena Fano, Matteo Gabburo +3
Vision Language Models (VLMs) achieved rapid progress in the recent years. However, despite their growth, VLMs development is heavily grounded on English, leading to two main limit…
Datasets for Multilingual Answer Sentence Selection
Matteo Gabburo, Stefano Campese, Federico Agostini +1
Answer Sentence Selection (AS2) is a critical task for designing effective retrieval-based Question Answering (QA) systems. Most advancements in AS2 focus on English due to the sca…
Measuring Retrieval Complexity in Question Answering Systems
Matteo Gabburo, Nicolaas Paul Jedema, Siddhant Garg +2
In this paper, we investigate which questions are challenging for retrieval-based Question Answering (QA). We (i) propose retrieval complexity (RC), a novel metric conditioned on t…
SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References
Matteo Gabburo, Siddhant Garg, Rik Koncel Kedziorski +1
Evaluation of QA systems is very challenging and expensive, with the most reliable approach being human annotations of correctness of answers for questions. Recent works (AVA, BEM)…
Learning Answer Generation using Supervision from Automatic Question Answering Evaluators
Matteo Gabburo, Siddhant Garg, Rik Koncel-Kedziorski +1
Recent studies show that sentence-level extractive QA, i.e., based on Answer Sentence Selection (AS2), is outperformed by Generation-based QA (GenQA) models, which generate answers…
Effective Pre-Training Objectives for Transformer-based Autoencoders
Luca Di Liello, Matteo Gabburo, Alessandro Moschitti
In this paper, we study trade-offs between efficiency, cost and accuracy when pre-training Transformer encoders with different pre-training objectives. For this purpose, we analyze…
Knowledge Transfer from Answer Ranking to Answer Generation
Matteo Gabburo, Rik Koncel-Kedziorski, Siddhant Garg +2
Recent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences (t…
Efficient pre-training objectives for Transformers
Luca Di Liello, Matteo Gabburo, Alessandro Moschitti
The Transformer architecture deeply changed the natural language processing, outperforming all previous state-of-the-art models. However, well-known Transformer models like BERT, R…