SOTAVerified

Reading Comprehension

Most current question answering datasets frame the task as reading comprehension where the question is about a paragraph or document and the answer often is a span in the document.

Some specific tasks of reading comprehension include multi-modal machine reading comprehension and textual machine reading comprehension, among others. In the literature, machine reading comprehension can be divide into four categories: cloze style, multiple choice, span prediction, and free-form answer. Read more about each category here.

Benchmark datasets used for testing a model's reading comprehension abilities include MovieQA, ReCoRD, and RACE, among others.

The Machine Reading group at UCL also provides an overview of reading comprehension tasks.

Figure source: A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets

Papers

Showing 14511500 of 1760 papers

TitleStatusHype
DEIM: An effective deep encoding and interaction model for sentence matching0
Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression0
Delta Embedding Learning0
Denoise while Aggregating: Collaborative Learning in Open-Domain Question Answering0
Dependency-Based Open Information Extraction0
Dependent Gated Reading for Cloze-Style Question Answering0
Deriving Commonsense Inference Tasks from Interactive Fictions0
Design and Challenges of Cloze-Style Reading Comprehension Tasks on Multiparty Dialogue0
Design and Development of an Online Computational Framework to Facilitate Language Comprehension Research on Indian Languages0
Designing a Tag-Based Statistical Math Word Problem Solver with Reasoning and Explanation0
Detecting Causes of Stock Price Rise and Decline by Machine Reading Comprehension with BERT0
Developing a How-to Tip Machine Comprehension Dataset and its Evaluation in Machine Comprehension by BERT0
Developing ARET: An NLP-based Educational Tool Set for Arabic Reading Enhancement0
Developing a Tutoring Dialog Dataset to Optimize LLMs for Educational Use0
Developing ChatGPT for Biology and Medicine: A Complete Review of Biomedical Question Answering0
Developing Dataset of Japanese Slot Filling Quizzes Designed for Evaluation of Machine Reading Comprehension0
Development and Evaluation of a Personalized Computer-aided Question Generation for English Learners to Improve Proficiency and Correct Mistakes0
DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension0
Dialog state tracking, a machine reading approach using Memory Network0
Dialog State Tracking: A Neural Reading Comprehension Approach0
Difficulty Controllable Generation of Reading Comprehension Questions0
DIFM:An effective deep interaction and fusion model for sentence matching0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?0
Discourse-Wide Extraction of Assay Frames from the Biological Literature0
Discrete Reasoning Templates for Natural Language Understanding0
Discriminative Joint Modeling of Lexical Variation and Acoustic Confusion for Automated Narrative Retelling Assessment0
DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach0
Distributed Vector Representations for Unsupervised Automatic Short Answer Grading0
Do Chinese models speak Chinese languages?0
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning0
Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension0
Document-Level N-ary Relation Extraction with Multiscale Representation Learning0
Document-Level N-ary Relation Extraction with Multiscale Representation Learning0
Does Structure Matter? Encoding Documents for Machine Reading Comprehension0
Do Large Language Models Mirror Cognitive Language Processing?0
Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction0
Domain-specific Language Pre-training for Dialogue Comprehension on Clinical Inquiry-Answering Conversations0
DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension0
DSReg: Using Distant Supervision as a Regularizer0
Dual Co-Matching Network for Multi-choice Reading Comprehension0
Dual Multi-head Co-attention for Multi-choice Reading Comprehension0
DUBLIN -- Document Understanding By Language-Image Network0
多模块联合的阅读理解候选句抽取(Evidence sentence extraction for reading comprehension based on multi-module)0
DuReader\_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications0
Dynamic Entity Representation with Max-pooling Improves Machine Reading0
Dynamic Fusion Networks for Machine Reading Comprehension0
Dynamic Sampling Strategies for Multi-Task Reading Comprehension0
DysList: An Annotated Resource of Dyslexic Errors0
ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task0
ECNU\_ICA\_1 SemEval-2021 Task 4: Leveraging Knowledge-enhanced Graph Attention Networks for Reading Comprehension of Abstract Meaning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6Unverified
2AMR-LE-EnsembleTest80Unverified
3MERIt(MERIt-deberta-v2-xxlarge )Test79.3Unverified
4MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3Unverified
5Knowledge modelTest79.2Unverified
6DeBERTa-v2-xxlarge-AMR-LE-ContrapositionTest77.2Unverified
7LReasoner ensembleTest76.1Unverified
8ELECTRA and ALBERTTest71Unverified
9WWZTest69.7Unverified
10xlnet-large-uncased [extended data]Test69.3Unverified
#ModelMetricClaimedVerifiedStatus
1ALBERT (Ensemble)Accuracy91.4Unverified
2Megatron-BERT (ensemble)Accuracy90.9Unverified
3ALBERTxxlarge+DUMA(ensemble)Accuracy89.8Unverified
4Megatron-BERTAccuracy89.5Unverified
5XLNetAccuracy (Middle)88.6Unverified
6DeBERTalargeAccuracy86.8Unverified
7B10-10-10Accuracy85.7Unverified
8RoBERTaAccuracy83.2Unverified
9Orca 2-13BAccuracy82.87Unverified
10Orca 2-7BAccuracy80.79Unverified
#ModelMetricClaimedVerifiedStatus
1Golden TransformerAverage F10.94Unverified
2MT5 LargeAverage F10.84Unverified
3ruRoberta-large finetuneAverage F10.83Unverified
4ruT5-large-finetuneAverage F10.82Unverified
5Human BenchmarkAverage F10.81Unverified
6ruT5-base-finetuneAverage F10.77Unverified
7ruBert-large finetuneAverage F10.76Unverified
8ruBert-base finetuneAverage F10.74Unverified
9RuGPT3XL few-shotAverage F10.74Unverified
10RuGPT3LargeAverage F10.73Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-LargeOverall: F164.4Unverified
2BERT-LargeOverall: F162.7Unverified
3BiDAFOverall: F128.5Unverified
#ModelMetricClaimedVerifiedStatus
1BERTMSE0.05Unverified
#ModelMetricClaimedVerifiedStatus
1BERT pretrained on MIMIC-IIIAnswer F163.55Unverified