Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: CS224N Project: Let Computers Do Reading Comprehension
Teaching Machines to Read and Comprehend
Identifying Where to Focus in Reading Comprehension for Neural Question Generation
Modeling Human Reading with Neural Attention
Multi-style Generative Reading Comprehension
A Deep Cascade Model for Multi-Document Reading Comprehension
Contextualized Word Representations for Reading Comprehension
Knowledge Based Machine Reading Comprehension
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Stochastic Answer Networks for Machine Reading Comprehension
Learning to Ask: Neural Question Generation for Reading Comprehension
Automatic assessment of student reading comprehension from short summaries
CS224n: Natural Language Processing with Deep Learning
DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension
Broad Context Language Modeling as Reading Comprehension
Effective Character-augmented Word Embedding for Machine Reading Comprehension
Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension
An LSTM Attention-based Network for Reading Comprehension
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
Visual Question Answering as Reading Comprehension
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension
A highly scalable Restricted Boltzmann Machine FPGA implementation
Calcium Currents in Hair Cells Isolated from Semicircular Canals of the Frog
Real-Time Obstacles Detection and Status Classification for Collision Warning in a Vehicle Active Safety System
Natural Language Processing (almost) from Scratch
Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base