| Challenge: | Existing research on machine reading comprehension rely heavily on large-size models and corpus to improve performance. |
| Approach: | They propose a framework that assesses model capabilities in an explainable and multi-dimensional manner. |
| Outcome: | The proposed framework achieves an 11.22% / 8.71% improvement of EM / F1 on MRC tasks. |
Similar Papers
Benchmarking Machine Reading Comprehension: A Psychological Perspective (2021.eacl-main)
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| Challenge: | MRC is a task that tests the ability of a machine to read and understand unstructured text. |
| Approach: | They propose a theoretical basis for the design of MRC datasets based on psychology and psychometrics and propose shortcut-proof questions and explanations as a part of the task design. |
| Outcome: | The proposed datasets should evaluate the model's ability to understand context-dependent situations and ensure substantive validity by shortcut-proof questions and explanation as a part of the task design. |
A Framework for Evaluation of Machine Reading Comprehension Gold Standards (2020.lrec-1)
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| Challenge: | Existing literature on machine reading comprehension (MRC) data is limited on the data design of gold standards. |
| Approach: | They propose a framework to investigate linguistic features, lexical cues and ambiguity in MRC gold standards. |
| Outcome: | The proposed framework investigates the present linguistic features, required reasoning and background knowledge and factual correctness on the one hand, and the presence of lexical cues as a lower bound for the requirement of understanding on the other. |
Coreference Reasoning in Machine Reading Comprehension (2021.acl-long)
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| Challenge: | Existing datasets for machine reading comprehension do not reflect the natural distribution and, consequently, the challenges of coreference reasoning. |
| Approach: | They propose to use existing coreference resolution datasets to train machine reading comprehension models to better reflect the natural distribution and, consequently, the challenges of coreference reasoning. |
| Outcome: | The proposed method improves the performance of state-of-the-art models on a set of coreference-related datasets. |
Improving Machine Reading Comprehension with General Reading Strategies (N19-1)
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| Challenge: | Recent studies have shown that reading strategies improve comprehension levels for readers lacking adequate prior knowledge. |
| Approach: | They propose three general strategies to improve machine reading comprehension (MRC) by fine-tuning a pre-trained model with strategies and a target task. |
| Outcome: | The proposed models improve non-extractive machine reading comprehension (MRC) on the largest general domain multiple-choice dataset RACE. |
Teaching Machine Comprehension with Compositional Explanations (2020.findings-emnlp)
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| Challenge: | Recent advances in machine reading comprehension rely heavily on large-scale annotated corpora, which are timeconsuming and costly to collect. |
| Approach: | They propose to use semi-structured explanations to “teach” machines reading comprehension using a small number of semi-structural explanations that explicitly inform machines why answer spans are correct. |
| Outcome: | The proposed method achieves 70.14% F1 score with supervision from 26 explanations on the SQuAD dataset, comparable to plain supervised learning using 1,100 labeled instances yielding a 12x speed up. |
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension (P19-1)
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| Challenge: | Recent results show pre-trained language models (LMs) can improve machine reading comprehension (MRC) Experimental results indicate that KT-NET offers significant and consistent improvements over BERT . |
| Approach: | They propose a method that leverages external knowledge bases to improve machine reading comprehension (MRC) KT-NET employs an attention mechanism to select desired knowledge from KBs and fuses selected knowledge with BERT to enable context- and knowledge-aware predictions. |
| Outcome: | The proposed model outperforms baseline models on ReCoRD and SQuAD1.1 benchmarks and ranks 1st on the ReCoDR and SQUAD1.1 leaderboards. |
Explicit Utilization of General Knowledge in Machine Reading Comprehension (P19-1)
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| Challenge: | Existing MRC models are unable to integrate general knowledge with human knowledge. |
| Approach: | They propose a data enrichment method which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair. |
| Outcome: | The proposed model outperforms state-of-the-art models and is robust to noise. |
What Makes Reading Comprehension Questions Easier? (D18-1)
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| Challenge: | Recent studies have shown that questions require a deeper understanding of language to answer beyond using superficial cues. |
| Approach: | They propose to use simple heuristics to split MRC datasets into easy and hard subsets and manually annotate questions from each subset with validity and reasoning skills to investigate which skills explain the difference between easy and harder questions. |
| Outcome: | The proposed model performs better for hard and easy questions than for easy questions. |
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)
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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. |
Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension (C18-1)
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| Challenge: | Existing MRC algorithms assume that each question is answerable by looking at text passages, but to realize human-like language comprehension ability, a machine should be able to distinguish not-answerable questions from answerable questions. |
| Approach: | They propose a method for automatically assigning difficulty level labels to a dataset that alters an existing MRC dataset and describes the resulting dataset. |
| Outcome: | The proposed method can detect NAQs in a dataset with difficulty level labels and is valid and potentially useful in the development of advanced MRC models. |