Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: A Compare-Aggregate Model with Dynamic-Clip Attention for Answer Selection
M$^2$S-Net: Multi-Modal Similarity Metric Learning based Deep Convolutional Network for Answer Selection
Knowledge-aware Attentive Neural Network for Ranking Question Answer Pairs
If You Can't Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking
Multi-Cast Attention Networks for Retrieval-based Question Answering and Response Prediction
Towards Predicting the Best Answers in Community-based Question-Answering Services
Essay-Anchor Attentive Multi-Modal Bilinear Pooling for Textbook Question Answering
A probabilistic graphical model for joint answer ranking in question answering
Probabilistic models for answer-ranking in multilingual question-answering
Improving Text Retrieval Precision and Answer Accuracy in Question Answering Systems
Attention-based Recurrent Neural Networks for Question Answering
Compositional Attention Networks for Interpretability in Natural Language Question Answering
Natural language annotations for question answering
LSTM-based Deep Learning Models for Answer Ranking
End-to-End Non-Factoid Question Answering with an Interactive Visualization of Neural Attention Weights
Multihop Attention Networks for Question Answer Matching
An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective
Harnessing Semantics for Answer Sentence Retrieval
Data-Driven Answer Selection in Community QA Systems
CMU OAQA at TREC 2016 LiveQA: An Attentional Neural Encoder-Decoder Approach for Answer Ranking
Structural relationships for large-scale learning of answer re-ranking
Novel RC sinusoidal oscillator using second-generation current conveyor
Multi-Batch Experience Replay for Fast Convergence of Continuous Action Control
3D Convolutional Generative Adversarial Networks for Detecting Temporal Irregularities in Videos
Inverse Cooking: Recipe Generation From Food Images
Mining and summarizing customer reviews