Compositionality of Complex Graphemes in the Undeciphered Proto-Elamite Script using Image and Text Embedding Models

Abstract

We introduce a language modeling architecture which operates over sequences of images, or over multimodal sequences of images with associated labels. We use this architecture alongside other embedding models to investigate a category of signs called complex graphemes (CGs) in the undeciphered proto-Elamite script. We argue that CGs have meanings which are at least partly compositional, and we discover novel rules governing the construction of CGs. We find that a language model over sign images produces more interpretable results than a model over text or over sign images and text, which suggests that the names given to signs may be obscuring signals in the corpus. Our results reveal previously unknown regularities in proto-Elamite sign use that can inform future decipherment efforts, and our image-aware language model provides a novel way to abstract away from biases introduced by human annotators.

Computational methods for undeciphered scripts

“…After the publication of Sign2Vec c , another approach has been proposed to perform clustering of signs using a variational autoencoder and a contextual component (Born et al, 2023). This approach is evaluated using DBSCAN and the obtained V-Measure is a slight improvement over Sign2Vec c on the Cypriot Greek sillabary, while using a fraction of the parameters.…”

“…In Born et al (2021), some of the same authors continued experimenting with computational techniques applied to Proto Elamite, focusing on an interesting feature of the writing system: complex graphemes. These are graphemes that appear to be composed from simpler ones, with various modes such as framing or inscription of one sign in another.…”

Decipherment as Regression: Solving Historical Substitution Ciphers by Learning Symbol Recurrence Relations

“…Computational decipherment based techniques have seen a wide range of applications ranging such as identifying unknown languages and scripts (Hauer and Kondrak, 2016), writing systems (Born et al, 2019(Born et al, , 2021 and lost languages (Snyder et al, 2010;Luo et al, 2019), offensive langauge detection (Wu et al, 2018;Qian et al, 2019), and, more recently, towards improving neural machine translation (Kambhatla et al, 2022). While decipherment has strong connections to cryptography research, we limit the scope of this work to natural language based decipherment.…”