  Language is an outcome of our complex and dynamic human-interactions and the
technique of natural language processing (NLP) is hence built on human
linguistic activities. Bidirectional Encoder Representations from Transformers
(BERT) has recently gained its popularity by establishing the state-of-the-art
scores in several NLP benchmarks. A Lite BERT (ALBERT) is literally
characterized as a lightweight version of BERT, in which the number of BERT
parameters is reduced by repeatedly applying the same neural network called
Transformer's encoder layer. By pre-training the parameters with a massive
amount of natural language data, ALBERT can convert input sentences into
versatile high-dimensional vectors potentially capable of solving multiple NLP
tasks. In that sense, ALBERT can be regarded as a well-designed
high-dimensional dynamical system whose operator is the Transformer's encoder,
and essential structures of human language are thus expected to be encapsulated
in its dynamics. In this study, we investigated the embedded properties of
ALBERT to reveal how NLP tasks are effectively solved by exploiting its
dynamics. We thereby aimed to explore the nature of human language from the
dynamical expressions of the NLP model. Our short-term analysis clarified that
the pre-trained model stably yields trajectories with higher dimensionality,
which would enhance the expressive capacity required for NLP tasks. Also, our
long-term analysis revealed that ALBERT intrinsically shows transient chaos, a
typical nonlinear phenomenon showing chaotic dynamics only in its transient,
and the pre-trained ALBERT model tends to produce the chaotic trajectory for a
significantly longer time period compared to a randomly-initialized one. Our
results imply that local chaoticity would contribute to improving NLP
performance, uncovering a novel aspect in the role of chaotic dynamics in human
language behaviors.
