Reading Comprehension
568 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
Most implemented papers
Know What You Don't Know: Unanswerable Questions for SQuAD
Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context.
Teaching Machines to Read and Comprehend
Teaching machines to read natural language documents remains an elusive challenge.
Reading Wikipedia to Answer Open-Domain Questions
facebookresearch/DrQA
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ACL 2017
This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article.
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
To demonstrate that large language models can further advance the state of the art (SOTA), we train an 8. 3 billion parameter transformer language model similar to GPT-2 and a 3. 9 billion parameter model similar to BERT.
Learning to Ask: Neural Question Generation for Reading Comprehension
xinyadu/nqg
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ACL 2017
We study automatic question generation for sentences from text passages in reading comprehension.
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
microsoft/DeBERTa
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ICLR 2021
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
A Unified MRC Framework for Named Entity Recognition
ShannonAI/mrc-for-flat-nested-ner
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ACL 2020
Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
We introduce an approach for open-domain question answering (QA) that retrieves and reads a passage graph, where vertices are passages of text and edges represent relationships that are derived from an external knowledge base or co-occurrence in the same article.
mT5: A massively multilingual pre-trained text-to-text transformer
google-research/multilingual-t5
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NAACL 2021
The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks.
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
Microsoft/SDNet
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Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context.
