中文摘要：

在这份报纸，我们在基于社区的问答处理答案检索问题。为了充分捕获在问题答案之间的相互作用，配对，我们建议原来的张肌为在他们之间的关联建模的神经网络。问题和候选人答案独立被嵌进不同潜伏的语义空格，并且 3 方法张肌然后被利用为在潜伏的语义之间的相互作用建模。适当地初始化网络层，我们建议一个新奇算法打电话降噪张肌 autoencoder (DTAE ) ，然后实现用降噪嵌入张肌层上的层和 DTAE 的词上的 autoencoders (DAE ) 的 layerwise pretraining 策略。试验性的结果显示出那我们神经网络超过的张肌有另外的竞争神经网络方法的各种各样的基线，和我们的 pretraining DTAE 策略改进系统性能和坚韧性。

英文摘要：

In this paper we address the answer retrieval problem in community-based question answering. To fully capture the interactions between question-answer pairs, we propose an original tensor neural network to model the relevance between them. The question and candidate answers are separately embedded into different latent semantic spaces, and a 3-way tensor is then utilized to model the interactions between latent semantics. To initialize the network layers properly, we propose a novel algorithm called denoising tensor autoencoder （DTAE）, and then implement a layerwise pretraining strategy using denoising autoencoders （DAE） on word embedding layers and DTAE on the tensor layer. The experimental results show that our tensor neural network outperforms various baselines with other competitive neural network methods, and our pretraining DTAE strategy improves the system＇s performance and robustness.