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

arXiv: https://arxiv.org/abs/2106.16013 · HTML: https://mlsanity.com/paper/2106.16013/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension](https://mlsanity.com/paper/1910.09753/) (2019)
- [End-to-End Open-Domain Question Answering with BERTserini](https://mlsanity.com/paper/1902.01718/) (2019)
- [MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension](https://mlsanity.com/paper/1905.13453/) (2019)
- [A Survey on Machine Reading Comprehension Systems](https://mlsanity.com/paper/2001.01582/) (2020)
- [Read + Verify: Machine Reading Comprehension with Unanswerable Questions](https://mlsanity.com/paper/1808.05759/) (2018)
- [Supervised and Unsupervised Transfer Learning for Question Answering](https://mlsanity.com/paper/1711.05345/) (2017)
- [MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models](https://mlsanity.com/paper/2005.02507/) (2020)
- [Adversarial Domain Adaptation for Machine Reading Comprehension](https://mlsanity.com/paper/1908.09209/) (2019)
- [Unsupervised Domain Adaptation of Language Models for Reading Comprehension](https://mlsanity.com/paper/1911.10768/) (2019)
- [Unsupervised Domain Adaptation on Reading Comprehension](https://mlsanity.com/paper/1911.06137/) (2019)
- [Improving QA Generalization by Concurrent Modeling of Multiple Biases](https://mlsanity.com/paper/2010.03338/) (2020)
- [BookQA: Stories of Challenges and Opportunities](https://mlsanity.com/paper/1910.00856/) (2019)

## Built on by (most cited first, 1 of 1)

- [Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systems](https://mlsanity.com/paper/2107.00368/) (2021)

## Abstract (arXiv)

One of the main challenges of the machine reading comprehension (MRC) models is their fragile out-of-domain generalization, which makes these models not properly applicable to real-world general-purpose question answering problems. In this paper, we leverage a zero-shot weighted ensemble method for improving the robustness of out-of-domain generalization in MRC models. In the proposed method, a weight estimation module is used to estimate out-of-domain weights, and an ensemble module aggregate several base models' predictions based on their weights. The experiments indicate that the proposed method not only improves the final accuracy, but also is robust against domain changes.