论文标题
检索，程序，重复：复杂的知识基础问题通过替代元学习回答
Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning
论文作者
论文摘要
一种令人信服的复杂问题回答方法是将问题转换为一系列动作，然后可以在知识库上执行以产生答案，也就是程序员互动方法。对测试问题使用类似的培训问题，Meta学习使程序员能够适应看不见的问题，以快速解决潜在的分配偏见。但是，这是以手动标记类似问题来学习检索模型的代价的，这是乏味而昂贵的。在本文中，我们提出了一种新颖的方法，该方法会自动与程序员相互学习的检索模型，即从弱监督下，即系统相对于产生的答案的性能。据我们所知，这是与程序员共同培训检索模型的首次尝试。我们的系统导致在大规模任务上的最新性能，以通过知识库回答复杂的问题。我们已经在https://github.com/devinjake/marl上发布了代码。
A compelling approach to complex question answering is to convert the question to a sequence of actions, which can then be executed on the knowledge base to yield the answer, aka the programmer-interpreter approach. Use similar training questions to the test question, meta-learning enables the programmer to adapt to unseen questions to tackle potential distributional biases quickly. However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive. In this paper, we present a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision, i.e., the system's performance with respect to the produced answers. To the best of our knowledge, this is the first attempt to train the retrieval model with the programmer jointly. Our system leads to state-of-the-art performance on a large-scale task for complex question answering over knowledge bases. We have released our code at https://github.com/DevinJake/MARL.
