Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systems

07/01/2021
by   Razieh Baradaran, et al.
0

Machine Reading Comprehension (MRC) is an active field in natural language processing with many successful developed models in recent years. Despite their high in-distribution accuracy, these models suffer from two issues: high training cost and low out-of-distribution accuracy. Even though some approaches have been presented to tackle the generalization problem, they have high, intolerable training costs. In this paper, we investigate the effect of ensemble learning approach to improve generalization of MRC systems without retraining a big model. After separately training the base models with different structures on different datasets, they are ensembled using weighting and stacking approaches in probabilistic and non-probabilistic settings. Three configurations are investigated including heterogeneous, homogeneous, and hybrid on eight datasets and six state-of-the-art models. We identify the important factors in the effectiveness of ensemble methods. Also, we compare the robustness of ensemble and fine-tuned models against data distribution shifts. The experimental results show the effectiveness and robustness of the ensemble approach in improving the out-of-distribution accuracy of MRC systems, especially when the base models are similar in accuracies.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

06/30/2021

Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization

One of the main challenges of the machine reading comprehension (MRC) mo...
08/23/2018

Attention-Guided Answer Distillation for Machine Reading Comprehension

Despite that current reading comprehension systems have achieved signifi...
03/28/2019

Sogou Machine Reading Comprehension Toolkit

Machine reading comprehension have been intensively studied in recent ye...
04/04/2021

ReCAM@IITK at SemEval-2021 Task 4: BERT and ALBERT based Ensemble for Abstract Word Prediction

This paper describes our system for Task 4 of SemEval-2021: Reading Comp...
11/09/2019

Improving Machine Reading Comprehension via Adversarial Training

Adversarial training (AT) as a regularization method has proved its effe...
05/07/2021

VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension

Existing models on Machine Reading Comprehension (MRC) require complex m...
12/07/2020

Semantics Altering Modifications for Evaluating Comprehension in Machine Reading

Advances in NLP have yielded impressive results for the task of machine ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.