Reading Comprehension
318 papers with code • 6 benchmarks • 89 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. The Machine Reading group at UCL also provides an overview of reading comprehension tasks.
Benchmarks
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SQuAD
MS MARCO
SuperGLUE
TriviaQA
bAbI
RACE
HotpotQA
NewsQA
CoQA
CBT
Subtasks
Greatest papers with code
Robust Reading Comprehension with Linguistic Constraints via Posterior Regularization
huggingface/pytorch-pretrained-BERT
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•
In this paper, we address the over-confidence issue and the over-sensitivity issue existing in current RC models simultaneously with the help of external linguistic knowledge.
mT5: A massively multilingual pre-trained text-to-text transformer
huggingface/transformers
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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.
Ranked #2 on
Reading Comprehension
on MuSeRC
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
huggingface/transformers
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NeurIPS 2020
With the success of language pretraining, it is highly desirable to develop more efficient architectures of good scalability that can exploit the abundant unlabeled data at a lower cost.
Ranked #6 on
Reading Comprehension
on RACE
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
huggingface/transformers
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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.
Ranked #1 on
Question Answering
on Quora Question Pairs
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.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
Ranked #2 on
Common Sense Reasoning
on SWAG
XLNet: Generalized Autoregressive Pretraining for Language Understanding
huggingface/transformers
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NeurIPS 2019
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
Ranked #1 on
Semantic Textual Similarity
on SentEval
Language Models are Unsupervised Multitask Learners
huggingface/transformers
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Preprint 2019
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
Ranked #1 on
Language Modelling
on enwik8
(using extra training data)
AllenNLP: A Deep Semantic Natural Language Processing Platform
This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding.
Reading Wikipedia to Answer Open-Domain Questions
facebookresearch/ParlAI
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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.
Ranked #1 on
Open-Domain Question Answering
on SQuAD1.1