Instruct: Given a paper title, retrieve paper titles that are cited by the given paper
Query: A Detailed Evaluation of Neural Sequence-to-Sequence Models for In-domain and Cross-domain Text Simplification.
Sentence Simplification with Deep Reinforcement Learning
Word Sequence Models for Single Text Summarization
Fast Domain Adaptation for Neural Machine Translation
Summarization Using Neural Networks
End-to-end Learning for Short Text Expansion
Domain specialization: a post-training domain adaptation for Neural Machine Translation
Text Simplification as Tree Labeling
Summarization with a Joint Model for Sentence Extraction and Compression
Syntactic Simplification and Text Cohesion
Neural Adaptation Layers for Cross-domain Named Entity Recognition
Extractive Text Summarization using Neural Networks
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering
Neural Machine Translation and Sequence-to-sequence Models: A Tutorial
AdaSum: an adaptive model for summarization
Pretraining-Based Natural Language Generation for Text Summarization
Text Simplification for Language Learners: A Corpus Analysis
Summarization using Attentive Sequence-to-Sequence RNNs
An Examination of the CNN / DailyMail Neural Summarization Task
Perplexity Minimization for Translation Model Domain Adaptation in Statistical Machine Translation
A Statistical Model For Domain-Independent Text Segmentation
Assigning change requests to software developers
RECURRENTNEURAL NETWORK REGULARIZATION
Novel Compact Ultra-wideband Bandpass Filter with Steep Skirt Selectivity
Course Assessment Practices and Student Learning Strategies in Online Courses
ACE: An Efficient Asynchronous Corner Tracker for Event Cameras