神经NLU系统中背景知识的动态集成
Dynamic Integration of Background Knowledge in Neural NLU Systems
摘要：
理解自然语言需要常识和背景知识，但在大多数神经自然语言理解（NLU）系统中，这些知识必须在学习期间从训练语料库中获取，然后在测试时是静态的。我们介绍了一种新的架构，在自然语言理解模型的显式背景知识的动态集成。通用阅读模块以自由文本语句（连同任务特定的文本输入）的形式读取背景知识，并将精炼的单词表示产生给任务特定的NLU架构，该NLU架构用这些表示重新处理任务输入。在文档问答（DQA）和文本蕴涵识别（RTE）上的实验证明了该方法的有效性和灵活性。分析表明，我们的模型学习以语义适当的方式利用知识。
英文摘要：
Common-sense and background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, this knowledge must be acquired from training corpora during learning, and then it is static at test time. We introduce a new architecture for the dynamic integration of explicit background knowledge in NLU models. A general-purpose reading module reads background knowledge in the form of free-text statements (together with task-specific text inputs) and yields refined word representations to a task-specific NLU architecture that reprocesses the task inputs with these representations. Experiments on document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of the approach. Analysis shows that our model learns to exploit knowledge in a semantically appropriate way.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
