近年来内容中心网络与区块链重点实验室优秀文章概览（一）
实验室经过这几年的积累，发表了不少优秀的文章，现在在这做一个展示概要，包括文章的简介，pdf链接以及部分文章的开源代码github链接。
COLING 2018国际会议
Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge
作者：Yang Deng, Ying Shen, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei
文章链接：https://aclanthology.coli.uni-saarland.de/papers/C18-1279/c18-1279
简介：答案选择是一项重要而具有挑战性的任务。在大量标记的训练数据可用的领域已经取得了显著的进展。然而，获得丰富的注释数据是耗时和昂贵的过程，将答案选择模型应用到具有有限标记数据的新领域将会有很大的障碍。在本文中，我们提出了知识感知注意力网络（KAN），一个跨领域答案选择的迁移学习框架，使用知识库作为桥梁，使知识从源领域转移到目标领域。具体地，我们设计了一个知识模块，将基于知识的表示学习集成到答案选择模型中。所学的基于知识的向量表示由源领域和目标领域共享，这不仅利用大量的跨领域数据，而且还受益于正则化效应，从而导致更通用的文本表示来帮助新领域中的任务。为了验证我们的模型的有效性，我们使用SQUAD-T数据集作为源域数据集和三个其他数据集（即yahoo QA，TREC QA和insuranceQA）作为目标域。实验结果表明，KAN具有很强的适用性和通用性，在跨域答案选择方面明显优于目前最好的模型算法。
Abstract
Answer selection is an important but challenging task. Significant progress has been made in domains where a large amount of labeled training data is available. However, obtaining rich annotated data is a time-consuming and expensive process, creating a substantial barrier for applying answer selection models to a new domain which has limited labeled data. In this paper, we propose Knowledge-aware Attentive Network (KAN), a transfer learning framework for crossdomain answer selection, which uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domains. Specifically, we design a knowledge module to integrate the knowledge-based representational learning into answer selection models. The learned knowledge-based representations are shared by source and target domains, which not only leverages large amounts of cross-domain data, but also benefits from...