MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension (D19-58)
Copied to clipboard
| Challenge: | MRQA datasets have been used to benchmark progress in general-purpose language understanding. |
| Approach: | They propose to combine 18 question answering datasets into one shared task to evaluate their generalization capabilities. |
| Outcome: | The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than baseline based on BERT. |
Similar Papers
MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension (P19-1)
Copied to clipboard
| Challenge: | A large number of reading comprehension (RC) datasets have been created, but little research has been done on whether they generalize to one another and the extent to which existing datasets can be leveraged for improving performance on new ones. |
| Approach: | They propose a BERT-based reading comprehension model that can be trained on multiple RC datasets. |
| Outcome: | The proposed model can be trained on multiple RC datasets and improve performance on five RC data. |
CLER: Cross-task Learning with Expert Representation to Generalize Reading and Understanding (D19-58)
Copied to clipboard
| Challenge: | In-domain datasets are used to train and validate our model, and other out-of-domain data are used for validation. |
| Approach: | They propose a model which uses cross-task learning with expert representation for the generalization of reading and understanding. |
| Outcome: | The proposed model achieved an average F1 score of 66.1 % in the out-of-domain setting, which is a 4.3 percentage point improvement over the official BERT baseline model. |
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)
Copied to clipboard
Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, Haifeng Wang
| Challenge: | MRC requires machines to understand text and answer questions about the text. |
| Approach: | They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models. |
| Outcome: | The proposed system is ranked at top 1 of all participants in terms of averaged F1 score. |
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method (2022.lrec-1)
Copied to clipboard
| Challenge: | Multiple-choice question answering (MCQA) requires a model to understand natural languages and understand textual representations. |
| Approach: | They propose a model that uses human reading comprehension attention to increase accuracy for machine reading comprehension. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Semeval-2018 Task 11 dataset and on the DREAM dataset. |
ARES: A Reading Comprehension Ensembling Service (2020.emnlp-demos)
Copied to clipboard
Anthony Ferritto, Lin Pan, Rishav Chakravarti, Salim Roukos, Radu Florian, J. William Murdock, Avi Sil
| Challenge: | ARES is a machine reading comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2.3 points. |
| Approach: | They propose a machine reading comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2.3 points. |
| Outcome: | The proposed system increases F1 by 2.3 points on a short answer task using an ensemble of models. |
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning (D19-58)
Copied to clipboard
| Challenge: | Existing methods focus on improving in-domain performance, leaving open the question of how they can generalize to out-of-domain and unseen RC tasks. |
| Approach: | They propose a multi-task learning framework that learns the shared representation across different tasks and builds on a large pre-trained language model and fine-tuned on multiple RC datasets. |
| Outcome: | The proposed framework improves the BERT-Large baseline by 8.39 and 7.22 respectively. |
A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension (D18-1)
Copied to clipboard
| Challenge: | Existing models of machine reading comprehension (MRC) are based on cloze style questions or crowdworkers given a short passage from well-edited sources. |
| Approach: | They propose a multi-answer multi-task framework that uses multiple reference answers for multiple questions. |
| Outcome: | The proposed model increases the ROUGE-L score on the DuReader dataset from 44.18, the previous state-of-the-art, to 51.09 . |
Comprehensive Multi-Dataset Evaluation of Reading Comprehension (D19-58)
Copied to clipboard
| Challenge: | Recent research aims to facilitate training and evaluation on several reading comprehension datasets at the same time. |
| Approach: | They propose an evaluation server that reports performance on seven diverse reading comprehension datasets and includes synthetic augmentations to test models' ability to handle out-of-domain questions. |
| Outcome: | The evaluation server performs on seven reading comprehension datasets, and collects and includes synthetic augmentations for these datasets to test models' ability to handle out-of-domain questions. |
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text (2020.emnlp-main)
Copied to clipboard
| Challenge: | MRC has achieved significant progress on the open domain in recent years due to large-scale pre-trained language models. |
| Approach: | They propose a machine reading comprehension model which exploits structural medical knowledge and reference medical plain text to improve the exam's accuracy. |
| Outcome: | The proposed model outperforms existing models with a large margin and passes the exam with 61.8% accuracy rate on the test set. |
Dataset for the First Evaluation on Chinese Machine Reading Comprehension (L18-1)
Copied to clipboard
| Challenge: | Existing reading comprehension datasets are mostly in English . |
| Approach: | They propose a Chinese reading comprehension dataset to add diversity to existing reading comprehension data . proposed dataset contains cloze-style reading comprehension and user query reading comprehension . |
| Outcome: | The proposed dataset is based on a Chinese reading comprehension dataset . it includes two types of cloze-style and user query reading comprehension . the proposed dataset hosted the 1st Evaluation on Chinese Machine Reading Comprehension (CMRC-2017) |