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.
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AllenNLP: A Deep Semantic Natural Language Processing Platform
This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding. AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily.
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. This task of machine reading at scale combines the challenges of document retrieval (finding the relevant articles) with that of machine comprehension of text (identifying the answer spans from those articles).
#120 best model for
Question Answering on SQuAD1.1
Embracing data abundance: BookTest Dataset for Reading Comprehension
There is a practically unlimited amount of natural language data available. 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. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation.
#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. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering.
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. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText.
COMMON SENSE REASONING DOCUMENT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION QUESTION ANSWERING READING COMPREHENSION
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. Recently, attention mechanisms have been successfully extended to MC.
#4 best model for
Question Answering on NarrativeQA
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention. 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.
#72 best model for
Question Answering on SQuAD1.1
MACHINE TRANSLATION QUESTION ANSWERING READING COMPREHENSION
Machine Comprehension Using Match-LSTM and Answer Pointer
• baidu/DuReader
• 
Machine comprehension of text is an important problem in natural language processing. We propose two ways of using Pointer Net for our task.
#133 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. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees.