Papers with MRC tasks
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data (2021.findings-emnlp)
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| Challenge: | despite considerable progress, most machine reading comprehension tasks lack sufficient training data to fully exploit powerful deep neural network models. |
| Approach: | They propose to use QA data to generate more training data for machine reading comprehension tasks by crowdsourcing . they first collect a large-scale multiple-choice QA dataset for Chinese, ExamQA, and then use incomplete, yet relevant snippets returned by a web search engine as the context for each QA instance. |
| Outcome: | The proposed model improves a Chinese MRC task with +5.1% accuracy and +3.8% exact match. |
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings (2021.starsem-1)
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| Challenge: | Neural networks are vulnerable to adversarial examples that have been mixed with certain perturbations. |
| Approach: | They propose a novel adversarial training method that perturbs the embedding matrix instead of word vectors to differentiate the roles of passages and questions. |
| Outcome: | The proposed method is effective universally and further improves the performance of MRC tasks. |
Learning Invariant Representation Improves Robustness for MRC Models (2022.findings-emnlp)
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| Challenge: | Existing approaches to improve machine reading comprehension models are vulnerable and not robust to adversarial examples. |
| Approach: | They propose to construct positive example pairs which have same answer by augmentation and then introduce stability and contrastive loss to improve invariance of representation. |
| Outcome: | The proposed approach boosts the robustness of QA models across different tasks and attack sets significantly and consistently. |
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension (N19-1)
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| Challenge: | Existing models for Machine Reading Comprehension (MRC) are small, compared to their size, and there are many studies on using pre-trained word embeddings and back-translation approaches to improve model generalization. |
| Approach: | They propose a multi-task learning framework to learn a machine reading comprehension model that can be applied to a wide range of MRC tasks in different domains. |
| Outcome: | The proposed model can be applied to a wide range of MRC tasks in different domains. |
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. |
Feeding What You Need by Understanding What You Learned (2022.acl-long)
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| 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. |
Incremental Transformer: Efficient Encoder for Incremented Text Over MRC and Conversation Tasks (2025.coling-main)
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| Challenge: | Existing encoders that encode incremented inputs have to re-encode the whole text to obtain the encoding of the extended input. |
| Approach: | They propose an efficient encoder dedicated for faster encoding of incremented input . it takes only added input as input but attends to cached representations of original input a lower layer . |
| Outcome: | The proposed encoder achieves 6.2x speedup over current encoders . it takes only added input as input but attends to cached representations of original input . |
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 . |