论文标题
从语言到语言：LSTM对荒谬的语言刺激的表示如何？
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?
论文作者
论文摘要
许多语言模型（单词嵌入，复发性神经网络和变形金刚）产生的表示与人们阅读时记录的大脑活动相关。但是，这些解码结果通常基于大脑对语法和语义上声音语言刺激的反应。在这项研究中，我们询问：LSTM（长期记忆）语言模型如何在语义和句法完整的语言上受过训练（总的来说），代表具有降级语义或句法信息的语言样本？ LSTM表示仍然类似于大脑的反应吗？我们发现，即使对于某种荒谬的语言，大脑活动与LSTM的表示之间也存在统计学意义的关系。这表明，至少在某些情况下，LSTMS和人脑的处理荒谬数据类似。
The representations generated by many models of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read. However, these decoding results are usually based on the brain's reaction to syntactically and semantically sound language stimuli. In this study, we asked: how does an LSTM (long short term memory) language model, trained (by and large) on semantically and syntactically intact language, represent a language sample with degraded semantic or syntactic information? Does the LSTM representation still resemble the brain's reaction? We found that, even for some kinds of nonsensical language, there is a statistically significant relationship between the brain's activity and the representations of an LSTM. This indicates that, at least in some instances, LSTMs and the human brain handle nonsensical data similarly.
