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. The Machine Reading group at UCL also provides an overview of reading comprehension tasks.
State-of-the-art leaderboards
Greatest papers with code
AllenNLP: A Deep Semantic Natural Language Processing Platform
This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding.
Language Models are Unsupervised Multitask Learners
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
SOTA for Language Modelling on Penn Treebank (Word Level) (using extra training data)
COMMON SENSE REASONING DOCUMENT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION QUESTION ANSWERING READING COMPREHENSION
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.
#106 best model for
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.
Teaching Machines to Read and Comprehend
NeurIPS 2015
• facebookresearch/ParlAI
•
Teaching machines to read natural language documents remains an elusive challenge.
#13 best model for
Question Answering
on CNN / Daily Mail
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent.
Bidirectional Attention Flow for Machine Comprehension
Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query.
#4 best model for
Question Answering
on MS MARCO
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models.
#62 best model for
Question Answering
on SQuAD1.1
MACHINE TRANSLATION QUESTION ANSWERING READING COMPREHENSION
Machine Comprehension Using Match-LSTM and Answer Pointer
• baidu/DuReader
• 
We propose two ways of using Pointer Net for our task.
#119 best model for
Question Answering
on SQuAD1.1
NATURAL LANGUAGE INFERENCE QUESTION ANSWERING READING COMPREHENSION
SQuAD: 100,000+ Questions for Machine Comprehension of Text
EMNLP 2016
• HKUST-KnowComp/R-Net
•
We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100, 000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage.