Paper Reading AI Learner

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

2022-01-15 06:21:01
Yoshitomo Matsubara, Luca Soldaini, Eric Lind, Alessandro Moschitti

Abstract

Large transformer models can highly improve Answer Sentence Selection (AS2) task, but their high computational costs prevent their use in many real world applications. In this paper, we explore the following research question: How can we make the AS2models more accurate without significantly increasing their model complexity? To address the question, we propose a Multiple Heads Student architecture (MHS), an efficient neural network designed to distill an ensemble of large transformers into a single smaller model. An MHS model consists of two components: a stack of transformer layers that is used to encode inputs, and a set of ranking heads; each of them is trained by distilling a different large transformer architecture. Unlike traditional distillation techniques, our approach leverages individual models in ensemble as teachers in a way that preserves the diversity of the ensemble members. The resulting model captures the knowledge of different types of transformer models by using just a few extra parameters. We show the effectiveness of MHS on three English datasets for AS2; our proposed approach outperforms all single-model distillations we consider, rivaling the state-of-the-art large AS2 models that have 2.7x more parameters and run 2.5x slower.

Abstract (translated)

URL

https://arxiv.org/abs/2201.05767

PDF

https://arxiv.org/pdf/2201.05767.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot