论文标题
可视化和解释语言模型
Visualizing and Explaining Language Models
论文作者
论文摘要
在过去的十年中，自然语言处理已成为计算机视觉之后的第二个人工智能领域，这是通过深度学习的出现大大改变的。无论架构如何，当天的语言模型都必须能够处理或生成文本，并根据任务预测缺失的单词，句子或关系。由于其黑盒性质，这种模型很难向第三方解释和解释。可视化通常是语言模型设计师用来解释其工作的桥梁，因为可以使用显着单词和短语，聚类或神经元激活的颜色来快速理解基础模型。本文展示了一些用于NLP可视化的最流行的深度学习中使用的技术，并特别关注解释性和解释性。
During the last decade, Natural Language Processing has become, after Computer Vision, the second field of Artificial Intelligence that was massively changed by the advent of Deep Learning. Regardless of the architecture, the language models of the day need to be able to process or generate text, as well as predict missing words, sentences or relations depending on the task. Due to their black-box nature, such models are difficult to interpret and explain to third parties. Visualization is often the bridge that language model designers use to explain their work, as the coloring of the salient words and phrases, clustering or neuron activations can be used to quickly understand the underlying models. This paper showcases the techniques used in some of the most popular Deep Learning for NLP visualizations, with a special focus on interpretability and explainability.
