Benchmarking Robustness of Machine Reading Comprehension Models (2021.findings-acl)
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| Challenge: | Existing benchmarks only evaluate models' robustness under test-time perturbations or adversarial attacks. |
| Approach: | They propose a model-agnostic benchmark to evaluate models' robustness under adversarial attacks. |
| Outcome: | The proposed model-agnostic benchmark evaluates models under four different types of adversarial attacks. |
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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. |
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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. |
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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. |
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Evaluating Neural Model Robustness for Machine Comprehension (2021.eacl-main)
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| Challenge: | evaluating model robustness to adversarial attacks can provide deeper understanding of how deep neural networks work and what kind of linguistic information is actually captured by neural networks. |
| Approach: | They propose a method for strategic sentence-level perturbations to evaluate model robustness to adversarial attacks using character and word perturbations. |
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Robust Machine Comprehension Models via Adversarial Training (N18-2)
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| Challenge: | Existing models for the Stanford Question Answering Dataset suffer from a 50% decrease in F1 score during adversarial evaluation based on AddSent. |
| Approach: | They propose an alternative adversary-generation algorithm, AddSentDiverse, that significantly increases the variance within the adversarial training data by providing effective examples that punish the model for making certain superficial assumptions. |
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The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models (2023.eacl-main)
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| Challenge: | Pretrained language models have achieved super-human performances on many Machine Reading Comprehension (MRC) benchmarks. |
| Approach: | They propose to fine-tune three state-of-the-art language models on SQuAD 1.1 or SQu AD 2.0 and then evaluate their robustness under adversarial attacks. |
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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. |
Robust Machine Reading Comprehension by Learning Soft labels (2020.coling-main)
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| Challenge: | Neural models have achieved great success on the task of machine reading comprehension, which are typically trained on hard labels. |
| Approach: | They propose a robust training method for machine reading comprehension models to address label sparseness problem by using three strategies to train models on soft labels. |
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Adversarial Domain Adaptation for Machine Reading Comprehension (D19-1)
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| Challenge: | Existing models for machine reading comprehension rely on large amounts of human-annotated in-domain data. |
| Approach: | They propose an unsupervised domain adaptation framework for Machine Reading Comprehension where the source domain has a large amount of labeled data, while only unlabeled passages are available in the target domain. |
| Outcome: | The proposed framework can be generalizable to different MRC models and datasets and can be extended to semi-supervised learning. |
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. |