LSTM-based Deep Learning Models for non-factoid answer selection

In this paper, we apply a general deep learning (DL) framework for the answer selection task, which does not depend on manually defined features or linguistic tools. The basic framework is to build the embeddings of questions and answers based on bidirectional long short-term memory (biLSTM) models, and measure their closeness by cosine similarity. We further extend this basic model in two directions. One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework. The other direction is to utilize a simple but efficient attention mechanism in order to generate the answer representation according to the question context. Several variations of models are provided. The models are examined by two datasets, including TREC-QA and InsuranceQA. Experimental results demonstrate that the proposed models substantially outperform several strong baselines.

在本文中，我们将一种通用的深度学习（DL）框架应用于答案选择任务，该框架不依赖于人工定义的特征或语言工具。基本框架是基于双向长短期记忆（biLSTM）模型构建问题和答案的嵌入，并通过余弦相似度来衡量它们的接近程度。我们进一步从两个方向扩展这个基本模型。一个方向是通过将卷积神经网络与基本框架相结合，为问题和答案定义一种更复合的表示形式。另一个方向是利用一种简单但有效的注意力机制，以便根据问题上下文生成答案表示。文中提供了几种不同的模型变体。这些模型通过两个数据集进行了检验，包括TREC - QA和InsuranceQA。实验结果表明，所提出的模型显著优于几个强大的基准模型。