46 citations · 111 across the 40 of their papers we have counts for
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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…
Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection
Luca Di Liello, Siddhant Garg, Luca Soldaini +1
An important task for designing QA systems is answer sentence selection (AS2): selecting the sentence containing (or constituting) the answer to a question from a set of retrieved…
Paragraph-based Transformer Pre-training for Multi-Sentence Inference
Luca Di Liello, Siddhant Garg, Luca Soldaini +1
Inference tasks such as answer sentence selection (AS2) or fact verification are typically solved by fine-tuning transformer-based models as individual sentence-pair classifiers. R…
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…
Question-Answer Sentence Graph for Joint Modeling Answer Selection
Roshni G. Iyer, Thuy Vu, Alessandro Moschitti +1
This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems. During offline learnin…
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.…
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems
Yoshitomo Matsubara, Luca Soldaini, Eric Lind +1
Large transformer models can highly improve Answer Sentence Selection (AS2) tasks, but their high computational costs prevent their use in many real-world applications. In this pap…