论文标题
偶然的监督：超越监督学习
Incidental Supervision: Moving beyond Supervised Learning
论文作者
论文摘要
在我们试图引起自然语言文本，视觉场景和其他凌乱的，自然发生的数据以及支持依赖它的决策的其他凌乱的自然语言文本，视觉场景和其他杂乱无章的决定中，机器学习和推理方法已变得无处不在。但是，这些任务的学习模型很难部分，部分原因是生成必要的监督信号是昂贵的，并且不扩展。本文介绍了几种旨在减轻监督瓶颈的学习范式。它将说明它们在多个问题的背景下的好处，这所有这些都与文本引起各种语义表示有关。
Machine Learning and Inference methods have become ubiquitous in our attempt to induce more abstract representations of natural language text, visual scenes, and other messy, naturally occurring data, and support decisions that depend on it. However, learning models for these tasks is difficult partly because generating the necessary supervision signals for it is costly and does not scale. This paper describes several learning paradigms that are designed to alleviate the supervision bottleneck. It will illustrate their benefit in the context of multiple problems, all pertaining to inducing various levels of semantic representations from text.
