Wiki ,中文名为“围纪”(注:不是“维基”,这是“维基媒体基金会”的注冊商标),是一种在网络上开放且可供多人协同创作的超文本系统,由沃德·坎宁安于 1995 年首先开发。沃德·坎宁安将 Wiki 定义为「一种允许一群用户利用简单的描述来创建和连接一组网页的社会计算系统」。


论文摘要: Multi-paragraph推理对于open-domain问答(OpenQA)是必不可少的,尽管在当前的OpenQA系统中受到的关注较少。在这项工作中,我们提出一个知识增强图神经网络(KGNN),使用实体对多个段落进行推理。为了显式地捕捉到实体的关系,KGNN利用关系事实知识图谱构建实体图谱。实验结果表明,与HotpotQA数据集上的基线方法相比,KGNN在分散注意力和完整的wiki设置方面都有更好的表现。我们进一步的分析表明,KGNN在检索更多的段落方面是有效和具有鲁棒性的。



Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios. One representative of such challenging scenarios is to deploy a translation system for a conference with a specific topic, e.g., global warming or coronavirus, where there are usually extremely less resources due to the limited schedule. To motivate wider investigation in such a scenario, we present a real-world fine-grained domain adaptation task in machine translation (FGraDA). The FGraDA dataset consists of Chinese-English translation task for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. Each sub-domain is equipped with a development set and test set for evaluation purposes. To be closer to reality, FGraDA does not employ any in-domain bilingual training data but provides bilingual dictionaries and wiki knowledge base, which can be easier obtained within a short time. We benchmark the fine-grained domain adaptation task and present in-depth analyses showing that there are still challenging problems to further improve the performance with heterogeneous resources.