(Not) Understanding Latin Poetic Style with Deep Learning
Ben Nagy
This article summarizes some mostly unsuccessful attempts to understand
authorial style by examining the attention of various neural networks (LSTMs
and CNNs) trained on a corpus of classical Latin verse that has been encoded to
include sonic and metrical features. Carefully configured neural networks are
shown to be extremely strong authorship classifiers, so it is hoped that they
might therefore teach `traditional' readers something about how the authors
differ in style. Sadly their reasoning is, so far, inscrutable. While the
overall goal has not yet been reached, this work reports some useful findings
in terms of effective ways to encode and embed verse, the relative strengths
and weaknesses of the neural network families, and useful (and not so useful)
techniques for designing and inspecting NN models in this domain. This article
suggests that, for poetry, CNNs are better choices than LSTMs -- they train
more quickly, have equivalent accuracy, and (potentially) offer better
interpretability. Based on a great deal of experimentation, it also suggests
that simple, trainable embeddings are more effective than domain-specific
schemes, and stresses the importance of techniques to reduce overfitting, like
dropout and batch normalization.