16 citations · 35 across the 21 of their papers we have counts for
31 papers
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…
DP-KB: Data Programming with Knowledge Bases Improves Transformer Fine Tuning for Answer Sentence Selection
Nic Jedema, Thuy Vu, Manish Gupta +1
While transformers demonstrate impressive performance on many knowledge intensive (KI) tasks, their ability to serve as implicit knowledge bases (KBs) remains limited, as shown on…
In Situ Answer Sentence Selection at Web-scale
Zeyu Zhang, Thuy Vu, Alessandro Moschitti
Current answer sentence selection (AS2) applied in open-domain question answering (ODQA) selects answers by ranking a large set of possible candidates, i.e., sentences, extracted f…
Double Retrieval and Ranking for Accurate Question Answering
Zeyu Zhang, Thuy Vu, Alessandro Moschitti
Recent work has shown that an answer verification step introduced in Transformer-based answer selection models can significantly improve the state of the art in Question Answering.…
Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answering
Siddhant Garg, Alessandro Moschitti
In this paper we propose a novel approach towards improving the efficiency of Question Answering (QA) systems by filtering out questions that will not be answered by them. This is…