Reading Comprehension Edit Task
Natural Language Processing
About
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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Subtasks
Datasets
Greatest papers with code
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
ICLR 2021
• huggingface/transformers
• 
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 SQuAD1.1 dev
COMMON SENSE REASONING COREFERENCE RESOLUTION LINGUISTIC ACCEPTABILITY NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING READING COMPREHENSION SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS WORD SENSE DISAMBIGUATION
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
NeurIPS 2020
• huggingface/transformers
•
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
Robust Reading Comprehension with Linguistic Constraints via Posterior Regularization
• huggingface/pytorch-pretrained-BERT
•
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.
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.
OPEN-DOMAIN QUESTION ANSWERING READING COMPREHENSION TEXT MATCHING
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
COMMON SENSE REASONING LANGUAGE MODELLING LEXICAL SIMPLIFICATION LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE QUESTION ANSWERING READING COMPREHENSION SENTIMENT ANALYSIS
XLNet: Generalized Autoregressive Pretraining for Language Understanding
NeurIPS 2019
• huggingface/transformers
•
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
DOCUMENT RANKING HUMOR DETECTION LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION QUESTION ANSWERING READING COMPREHENSION SENTIMENT ANALYSIS TEXT CLASSIFICATION
Language Models are Unsupervised Multitask Learners
Preprint 2019
• huggingface/transformers
•
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)
4 COMMON SENSE REASONING DATA-TO-TEXT GENERATION DOCUMENT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION MULTI-TASK LEARNING QUESTION ANSWERING READING COMPREHENSION
AllenNLP: A Deep Semantic Natural Language Processing Platform
This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding.
NATURAL LANGUAGE UNDERSTANDING READING COMPREHENSION SEMANTIC ROLE LABELING
Reading Wikipedia to Answer Open-Domain Questions
ACL 2017
• facebookresearch/ParlAI
•
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
Embracing data abundance: BookTest Dataset for Reading Comprehension
We show that training on the new data improves the accuracy of our Attention-Sum Reader model on the original CBT test data by a much larger margin than many recent attempts to improve the model architecture.