Recurrent Neural Network Encoder with Attention for Community Question Answering
arXiv:1603.07044
Abstract
We apply a general recurrent neural network (RNN) encoder framework to community question answering (cQA) tasks. Our approach does not rely on any linguistic processing, and can be applied to different languages or domains. Further improvements are observed when we extend the RNN encoders with a neural attention mechanism that encourages reasoning over entire sequences. To deal with practical issues such as data sparsity and imbalanced labels, we apply various techniques such as transfer learning and multitask learning. Our experiments on the SemEval-2016 cQA task show 10% improvement on a MAP score compared to an information retrieval-based approach, and achieve comparable performance to a strong handcrafted feature-based method.
References in corpus (6)
- Sequence to Sequence Learning with Neural Networks
- LSTM: A Search Space Odyssey
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Reasoning about Entailment with Neural Attention
- A large annotated corpus for learning natural language inference
- Applying Deep Learning to Answer Selection: A Study and An Open Task
Cited by in corpus (3)
- Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection
- No Need to Pay Attention: Simple Recurrent Neural Networks Work! (for Answering "Simple" Questions)
- Deep Feature Fusion Network for Answer Quality Prediction in Community Question Answering