"The Cascade Transformer: an Application for Efficient Answer Sentence Selection" ACL 2020
Aug 26, 202022 views
This is a recorded talk for "The Cascade Transformer: an Application for Efficient Answer Sentence Selection" a work by Luca Soldaini and Alessandro Moschitti accepted as long paper at ACL 2020. PDF of the ACL Anthology: https://www.aclweb.org/anthology/2020.acl-main.504/ Link to virtual conference: https://virtual.acl2020.org/paper_main.504.html Abstract: Large transformer-based language models have been shown to be very effective in many classification tasks. However, their computational complexity prevents their use in applications requiring the classification of a large set of candidates. While previous works have investigated approaches to reduce model size, relatively little attention has been paid to techniques to improve batch throughput during inference. In this paper, we introduce the Cascade Transformer, a simple yet effective technique to adapt transformer-based models into a cascade of rankers. Each ranker is used to prune a subset of candidates in a batch, thus dramatically increasing throughput at inference time. Partial encodings from the transformer model are shared among rerankers, providing further speed-up. When compared to a state-of-the-art transformer model, our approach reduces computation by 37% with almost no impact on accuracy, as measured on two English Question Answering datasets.