| 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. |
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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. |
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior (D19-58)
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| Challenge: | Recent studies indicate that the current machine reading comprehension systems suffer from weak robustness against adversarial samples. |
| Approach: | They propose to take sentence syntax as the leverage in the answer predicting process and exploit the syntactic elements of a question to improve the generalization and robustness of MRC models. |
| Outcome: | The proposed method improves generalization and robustness against adversarial samples, with performance well-maintained. |
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. |
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension (2020.acl-main)
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| Challenge: | Existing approaches to machine reading comprehension (MRC) on long texts typically chunk text into equally-spaced segments without considering information from other segments. |
| Approach: | They propose to let a model learn to chunk in a more flexible way via reinforcement learning. |
| Outcome: | The proposed model extracts a text span from document and query as answer . previous models can only take a fixed-length (e.g., 512) text as input . |
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. |
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension (2020.acl-main)
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| Challenge: | Existing approaches to improve machine reading comprehension performance on low resource languages are limited due to the lack of sufficient training data. |
| Approach: | They propose to use a mixed MRC task to translate the question to other languages and build cross-lingual question-passage pairs. |
| Outcome: | The proposed task improves on two cross-lingual MRC datasets. |
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. |
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. |
Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models (2021.acl-short)
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| Challenge: | Pre-trained language models have achieved human-level performance on many Machine Reading Comprehension (MRC) tasks, but it remains unclear whether these models truly understand language or answer questions by exploiting statistical biases in datasets. |
| Approach: | They propose a method to attack MRC models by exposing statistical biases in a RACE dataset and propose an augmented training method that can greatly reduce models’ statistical bias. |
| Outcome: | The proposed method can reduce models’ statistical biases from human-level performance to chance-level. |
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension (2024.emnlp-main)
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| Challenge: | Extractive Machine Reading Comprehension (MRC) is a challenging field in the field of Natural Language Processing. |
| Approach: | They propose a Question-Attended Span Extraction module to address the limitations of generative approaches for extractive machine reading comprehension (MRC) . module significantly enhances performance of pre-trained generative language models, enabling them to surpass the extractive capabilities of advanced Large Language Models (LLMs) |
| Outcome: | The QASE module surpasses state-of-the-art models in few-shot settings. |