# Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering

arXiv: https://arxiv.org/abs/1506.06490 · HTML: https://mlsanity.com/paper/1506.06490/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2015.

## Built on by (most cited first, 2 of 2)

- [Generate Natural Language Explanations for Recommendation](https://mlsanity.com/paper/2101.03392/) (2021)
- [Best-Answer Prediction in Q&A Sites Using User Information](https://mlsanity.com/paper/2212.08475/) (2022)

## Abstract (arXiv)

In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem. Our approach applies convolution neural networks (CNNs) to learning the joint representation of question-answer pair firstly, and then uses the joint representation as input of the long short-term memory (LSTM) to learn the answer sequence of a question for labeling the matching quality of each answer. Experiments conducted on the SemEval 2015 CQA dataset shows the effectiveness of our approach.