Attempts to Understand Latin Poetic Style Using Deep Learning Classifiers

Kernekoncepter

Despite carefully configured neural networks achieving high accuracy in classifying the authorship of classical Latin verse, the reasoning behind their decisions remains inscrutable, failing to provide meaningful insights into poetic style.

Resumé

The article summarizes experiments using neural networks, specifically LSTMs and CNNs, to classify the authorship of a corpus of classical Latin hexameter verse. The goal was to leverage the strong classification performance of these models to potentially gain insights into the stylistic differences between authors.
Key highlights:

A moderately sized corpus of Latin hexameter verse from various authors was used as the dataset.
Syllable-level tokenization and embedding were explored as effective ways to encode the data for neural networks.
Techniques like dropout, batch normalization, and parameter starvation were crucial to control overfitting and improve generalization.
The neural networks achieved high classification accuracy, around 97-98%, comparable to or exceeding human experts.
However, attempts to interpret the models' decision-making process through attention visualization were largely unsuccessful.
Visualizing attention at the dense layer was found to be ineffective, as the attention was mostly determined by the assigned class rather than the content.
Visualizing attention at the convolutional layers also did not yield consistent, interpretable insights into the models' understanding of poetic style.
The author concludes that the reasoning behind the neural networks' strong classification performance remains inscrutable, and the gap between quantitative description of stylistic elements and the human perception of poetic style remains vast.
Future work suggestions include further examination of the syllable embeddings and deriving measures of stylistic similarity or distinctiveness for authorship attribution tasks.

Statistik

Statius 7:546: Taenariis admittere manes Thessala vates
Statius 7:730: Taenariis admittere manes Thessala vates
Ovid 9:211: Taenariis admittere manes Thessala vates
Silius 10:363: Taenariis admittere manes Thessala vates
Vergil 3:123: Taenariis admittere manes Thessala vates
Lucan 6:419: Taenariis admittere manes Thessala vates

Citater

"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."

Vigtigste indsigter udtrukket fra

(Not) Understanding Latin Poetic Style with Deep Learning

by Ben Nagy kl. arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06150.pdf

Dybere Forespørgsler

How might generative models or natural language processing approaches be leveraged to extract the neural networks' understanding of poetic style in a more interpretable way?

Generative models and natural language processing (NLP) approaches can offer valuable insights into the neural networks' comprehension of poetic style by providing a more interpretable framework. Generative models, such as variational autoencoders (VAEs) or generative adversarial networks (GANs), can be employed to generate synthetic poetic texts based on the learned stylistic features. By analyzing the generated texts, researchers can gain a deeper understanding of the latent representations captured by the neural networks.
In the context of NLP, techniques like attention mechanisms and transformer models can be utilized to visualize the neural networks' focus on specific elements within the poetic texts. Attention mechanisms allow researchers to identify which parts of the input data are most influential in making classification decisions. By visualizing the attention weights, one can interpret how the neural networks process and prioritize different aspects of the poetic style.
Furthermore, sentiment analysis and semantic similarity measures can be applied to assess the emotional and thematic nuances present in the poetic texts. By examining the sentiment polarity and semantic relationships between different poems, researchers can uncover underlying patterns that contribute to the classification of authorial style.
Overall, leveraging generative models and NLP approaches can enhance the interpretability of neural networks in understanding poetic style by providing visualizations, synthetic text generation, and sentiment analysis tools.

What are the philosophical and neuroscientific implications if the "poetic feel" that humans perceive is an emergent property requiring vastly more complicated models to emulate?

The notion that the "poetic feel" perceived by humans is an emergent property that necessitates significantly complex models to replicate has profound philosophical and neuroscientific implications. Philosophically, it raises questions about the nature of creativity, consciousness, and the essence of human expression. If capturing the essence of poetic style demands intricate models beyond current computational capabilities, it suggests that there are intricate cognitive processes and emotional depths involved in the creation and appreciation of poetry that may transcend algorithmic understanding.
From a neuroscientific perspective, the idea that the "poetic feel" is an emergent property requiring sophisticated models implies that the human brain operates on intricate neural networks and cognitive mechanisms that are not fully understood. It suggests that the processing of poetic language involves multi-layered neural connections, emotional responses, and cognitive functions that are challenging to replicate artificially. This complexity underscores the richness of human cognition and the intricate interplay between language, emotion, and creativity.
Moreover, the need for vastly more complicated models to emulate the "poetic feel" highlights the limitations of current computational approaches in capturing the subtleties and nuances of human expression. It underscores the uniqueness of human creativity and the intricate interplay between linguistic, emotional, and aesthetic dimensions in the realm of poetry.

Could the properties of the syllable embeddings, particularly their linear combinations and topological relationships, provide additional insights into the neural networks' conception of poetic style?

The properties of syllable embeddings, including their linear combinations and topological relationships, hold significant potential to offer additional insights into the neural networks' conception of poetic style. By analyzing the linear combinations of syllable embeddings, researchers can uncover underlying patterns and relationships that contribute to the classification of authorial style. The linear combinations may reveal specific phonetic or metrical features that are characteristic of certain authors or poetic traditions, shedding light on the stylistic nuances captured by the neural networks.
Moreover, exploring the topological relationships within the syllable embeddings can provide a deeper understanding of the structural organization of poetic texts. By visualizing the clustering of syllables based on their phonetic and metrical properties, researchers can identify commonalities and distinctions in authorial styles. The topological relationships may reveal subtle nuances in rhythm, rhyme, or sound patterns that influence the classification of poetic texts.
Overall, delving into the properties of syllable embeddings, such as their linear combinations and topological relationships, can enrich our understanding of how neural networks perceive and differentiate poetic styles. By examining these properties, researchers can uncover hidden patterns and stylistic elements that contribute to the neural networks' conception of poetic style.