Examining the Authenticity of Plato’s Epistle VII through Deep Learning

Abstract

Plato’s Epistle VII, a text in which the famous Athenian philosopher describes his political involvement in the affairs of 4th-century B.C.E Syracuse, has long been considered dubious by classical philologists. In particular, scholars have scrutinized two sections of the letter, in the first of which Plato gives political advice contrary to other claims made in his other works, and in the second of which Plato digresses from his political narrative to discuss a philosophical doctrine known as the Theory of Forms. Specifically, some scholars have raised the possibility of textual interpolation, whereby inauthentic passages might have been added to an otherwise authentic text.

This paper sets out to apply computational methodology from deep learning to provide further insight on such a long-standing problem in Platonic scholarship. As such, I developed a bidirectional long-short-term memory (LSTM) recurrent neural network (RNN) with trainable word embeddings to classify units of roughly 100 words of Ancient Greek text as belonging to Plato or one of six other Ancient Greek prose authors. Given Ancient Greek’s rich morphology, special care was taken to formulate an optimal pre-processing approach: of four methods — plaintext, lemmatization, byte-pair encoding (BPE), and a lemmatization-BPE ensemble — the ensemble exhibited the highest test accuracy (89.28%), improving significantly upon a Naïve Bayes baseline model (70.93%). Applied to Epistle VII, this model reveals that the letter seems mostly authentic, except for two markedly more spurious sections, one of which corresponds nearly perfectly with the boundaries of the section consisting of political advice to the Sicilians. Such a result provides further support to the pre-existing claim that this section is an interpolation by a non-Platonic author within an otherwise Platonic text.