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. |
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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. |
Cross-Task Knowledge Transfer for Query-Based Text Summarization (D19-58)
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| Challenge: | Existing methods for summarization data corpora are limited to extractive and abstractive summarizing. |
| Approach: | They propose to use machine reading comprehension (MRC) and query-based text summarization to produce extractive and abstractive summaries from pre-trained MRC and MT models. |
| Outcome: | The proposed model outperforms existing methods on CNN/Daily Mail and Debatepedia datasets and can be used as a baseline for future systems. |
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. |
A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction (2020.acl-main)
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| Challenge: | Existing models for machine reading comprehension lack evidence labels for training models. |
| Approach: | They propose a method which supervises the evidence extractor with auto-generated evidence labels in an iterative process. |
| Outcome: | The proposed method improves on three MRC tasks on seven datasets. |
REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training (2021.findings-acl)
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| Challenge: | Pre-trained language models have achieved great success on Machine Reading Comprehension (MRC) however, the poor support in evidence extraction hinders them from further advancing MRC. |
| Approach: | They propose a REtrieval-based pre-training approach that strengthens evidence extraction during pre-training by inherited downstream MRC tasks. |
| Outcome: | The proposed approach strengthens evidence extraction during pre-training, which is further inherited by downstream tasks. |
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications (2021.acl-short)
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| Challenge: | In order to comprehensively verify the robustness and generalization of MRC models, we construct a real-world Chinese dataset - DuReader_robust . |
| Approach: | They introduce a real-world Chinese dataset to evaluate the robustness and generalization of MRC models from three aspects: over-sensitivity, over-stability and generalisation. |
| Outcome: | The proposed model fails to perform well on the challenge test set and may provide suggestions for future model development. |
Machine Reading Comprehension using Case-based Reasoning (2023.findings-emnlp)
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Dung Thai, Dhruv Agarwal, Mudit Chaudhary, Wenlong Zhao, Rajarshi Das, Jay-Yoon Lee, Hannaneh Hajishirzi, Manzil Zaheer, Andrew McCallum
| Challenge: | Current state-of-the-art machine readers do not support case-based reasoning . |
| Approach: | They propose a method that extracts a set of similar cases from a nonparametric memory and then predicts an answer by selecting the span in the test context that is most similar to the contextualized representations of answers. |
| Outcome: | The proposed method outperforms baselines on NaturalQuestions and NewsQA by 11.5 and 8.4 EM. |
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge (2022.acl-long)
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| Challenge: | Existing methods to improve machine reading comprehension (MRC) tasks require unstated knowledge to perform well. |
| Approach: | They propose to extract a new kind of structured knowledge from scripts and use it to improve machine reading comprehension (MRC) They propose a teacher-student paradigm to facilitate the transfer of knowledge in weakly-labeled MRC data. |
| Outcome: | The proposed method outperforms methods that use weakly-labeled data and improves a state-of-the-art baseline by 4.3% in accuracy on a Chinese multiple-choice MRC dataset C3. |
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. |
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification (P18-1)
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| Challenge: | Recent years have seen rapid growth in the MRC community . MRC is believed to be a crucial step in building a general intelligent agent . |
| Approach: | They propose an end-to-end neural model that enables multiple passages to verify each other based on their content representations. |
| Outcome: | The proposed model outperforms the baseline on the English MS-MARCO dataset and the Chinese DuReader dataset, and achieves state-of-the-art performance on both datasets. |