Search Results for author: Eric Lind

Found 3 papers, 1 papers with code

Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems

1 code implementation15 Jan 2022 Yoshitomo Matsubara, Luca Soldaini, Eric Lind, Alessandro Moschitti

CERBERUS consists of two components: a stack of transformer layers that is used to encode inputs, and a set of ranking heads; unlike traditional distillation technique, each of them is trained by distilling a different large transformer architecture in a way that preserves the diversity of the ensemble members.

Efficient Neural Network Question Answering +1

Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation

no code implementations14 Oct 2021 Benjamin Muller, Luca Soldaini, Rik Koncel-Kedziorski, Eric Lind, Alessandro Moschitti

Our cross-lingual generative system outperforms answer sentence selection baselines for all 5 languages and monolingual generative pipelines for three out of five languages studied.

Answer Generation Generative Question Answering +3

Answer Generation for Retrieval-based Question Answering Systems

no code implementations Findings (ACL) 2021 Chao-Chun Hsu, Eric Lind, Luca Soldaini, Alessandro Moschitti

Recent advancements in transformer-based models have greatly improved the ability of Question Answering (QA) systems to provide correct answers; in particular, answer sentence selection (AS2) models, core components of retrieval-based systems, have achieved impressive results.

Answer Generation Question Answering +2

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