Reading Comprehension
569 papers with code • 7 benchmarks • 95 datasets
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
Benchmarks
These leaderboards are used to track progress in Reading Comprehension
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Libraries
Use these libraries to find Reading Comprehension models and implementations
SQuAD
MS MARCO
TriviaQA
HotpotQA
RACE
NewsQA
CoQA
XQuAD
QuAC
TyDiQA
Subtasks
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Machine Reading Comprehension
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Intent Recognition
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Implicit Relations
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LAMBADA
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LAMBADA
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Question Selection
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Multi-Hop Reading Comprehension
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Implicatures
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Logical Reasoning Reading Comprehension
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English Proverbs
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Fantasy Reasoning
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Figure Of Speech Detection
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Formal Fallacies Syllogisms Negation
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GRE Reading Comprehension
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Hyperbaton
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Movie Dialog Same Or Different
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Nonsense Words Grammar
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Phrase Relatedness
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RACE-h
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RACE-m
Latest papers with no code
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training
In medical information extraction, medical Named Entity Recognition (NER) is indispensable, playing a crucial role in developing medical knowledge graphs, enhancing medical question-answering systems, and analyzing electronic medical records.
Knowledge Condensation and Reasoning for Knowledge-based VQA
We condense the retrieved knowledge passages from two perspectives.
CuentosIE: can a chatbot about "tales with a message" help to teach emotional intelligence?
In this article, we present CuentosIE (TalesEI: chatbot of tales with a message to develop Emotional Intelligence), an educational chatbot on emotions that also provides teachers and psychologists with a tool to monitor their students/patients through indicators and data compiled by CuentosIE.
Towards a Psychology of Machines: Large Language Models Predict Human Memory
Participants, both human and ChatGPT, were presented with pairs of sentences.
SaulLM-7B: A pioneering Large Language Model for Law
In this paper, we introduce SaulLM-7B, a large language model (LLM) tailored for the legal domain.
AceMap: Knowledge Discovery through Academic Graph
The exponential growth of scientific literature requires effective management and extraction of valuable insights.
Choose Your Own Adventure: Interactive E-Books to Improve Word Knowledge and Comprehension Skills
Students read two e-Books that taught word learning and comprehension monitoring strategies in the service of learning difficult vocabulary and targeted science concepts about hurricanes.
Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
This research is motivated by the potential of LLMs to predict learning performance based on its inherent reasoning and computational capabilities.
Do Large Language Models Mirror Cognitive Language Processing?
We employ Representational Similarity Analysis (RSA) to mearsure the alignment between 16 mainstream LLMs and fMRI signals of the brain.
QASE Enhanced PLMs: Improved Control in Text Generation for MRC
To address the challenges of out-of-control generation in generative models for machine reading comprehension (MRC), we introduce the Question-Attended Span Extraction (QASE) module.
