How related are the representations learned by neural language models, translation models, and language tagging tasks? We answer this question by adapting an encoder-decoder transfer learning method from computer vision to investigate the structure among 100 different feature spaces extracted from hidden representations of various networks trained on language tasks. This method reveals a low-dimensional structure where language models and translation models smoothly interpolate between word embeddings, syntactic and semantic tasks, and future word embeddings. We call this low-dimensional structure a language representation embedding because it encodes the relationships between representations needed to process language for a variety of NLP tasks. We find that this representation embedding can predict how well each individual feature space maps to human brain responses to natural language stimuli recorded using fMRI. Additionally, we find that the principal dimension of this structure can be used to create a metric which highlights the brain's natural language processing hierarchy. This suggests that the embedding captures some part of the brain's natural language representation structure.

翻译：神经语言模型、 翻译模型和语言标记任务所学的表达方式如何相关? 我们通过修改编码器- 解码器从计算机视野中传输学习方法来回答这个问题, 以调查从受过语言任务培训的各种网络的隐蔽表达方式中提取的100个不同特征空间的结构。 这个方法揭示了一个低维结构, 语言模型和翻译模型在文字嵌入、 合成和语义任务以及未来的嵌入词之间可以顺利地相互交织。 我们把这个低维结构称为语言嵌入, 因为它将处理各种 NLP 任务所需的语言的表达方式之间的关系编码起来。 我们发现, 这种嵌入可以预测每个单个特征的空间地图对人类大脑使用FMRI 记录的自然语言模拟反应的效果有多好。 此外, 我们发现, 这个结构的主要层面可以用来创建一种指标, 来突出大脑的自然语言处理等级。 这意味着嵌入过程可以捕捉到大脑自然语言代表结构的某些部分 。