论文标题
零照片问题的自我监督知识三胞胎学习回答
Self-supervised Knowledge Triplet Learning for Zero-shot Question Answering
论文作者
论文摘要
所有问答（QA）系统的目的是能够概括地看不见的问题。当前的监督方法依赖于昂贵的数据注释。此外，这样的注释可以引入意外的注释偏差，这使系统更关注偏见，而不是实际任务。在这项工作中，我们提出了知识三胞胎学习（KTL），这是一项自我监督的任务，对知识图。我们建议启发式方法为常识和科学知识创建合成图。我们提出了如何使用KTL执行零射击质量质量检查的方法，我们的实验比大型预训练的变压器模型显示出显着改善。
The aim of all Question Answering (QA) systems is to be able to generalize to unseen questions. Current supervised methods are reliant on expensive data annotation. Moreover, such annotations can introduce unintended annotator bias which makes systems focus more on the bias than the actual task. In this work, we propose Knowledge Triplet Learning (KTL), a self-supervised task over knowledge graphs. We propose heuristics to create synthetic graphs for commonsense and scientific knowledge. We propose methods of how to use KTL to perform zero-shot QA and our experiments show considerable improvements over large pre-trained transformer models.
