# Recurrent Neural Network Encoder with Attention for Community Question Answering

Source: [Recurrent Neural Network Encoder with Attention for Community Question Answering](<https://www.sota2.com/research/paper/68fa625b1e209131ce9f8c10>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:17:31.370Z

Authors: Wei-Ning Hsu, Yu Zhang, James Glass

Published: 2016-03-23T01:52:54.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1603.07044>)

[PDF](<https://arxiv.org/pdf/1603.07044.pdf>)