Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs

Deciphering Undersegmented Ancient Scripts Using Phonetic Prior Jiaming Luo CSAIL, MIT j luo@csail.mit.edu Frederik Hartmann University of Konstanz frederik.hartmann @uni-konstanz.de Enrico Santus Bayer enrico.santus@bayer.com Regina Barzilay CSAIL, MIT regina@csail.mit.edu Yuan Cao Google Brain yuancao@google.com Abstract Most undeciphered lost languages exhibit two characteristics that pose significant decipher- ment challenges: (1) the scripts are not fully segmented into words; (2) the closest known language is not determined. We propose a decipherment model that handles both of these challenges by building on rich linguis- tic constraints reflecting consistent patterns in historical sound change. We capture the natural phonological geometry by learning character embeddings based on the International Pho- netic Alphabet (IPA). The resulting generative framework jointly models word segmentation and cognate alignment, informed by phono- logical constraints. We evaluate the model on both deciphered languages (Gothic, Ugaritic) and an undeciphered one (Iberian). The ex- periments show that incorporating phonetic geometry leads to clear and consistent gains. Additionally, we propose a measure for lan- guage closeness which correctly identifies related languages for Gothic and Ugaritic. For Iberian, the method does not show strong evi- dence supporting Basque as a related language, concurring with the favored position by the current scholarship.1 1 Introduction All the known cases of lost language decipherment have been accomplished by human experts, oftentimes over decades of painstaking efforts. At least a dozen languages are still undeciphered today. For some of those languages, even the most fundamental questions pertaining to their origins 1Code and data available at https://github.com /j-luo93/DecipherUnsegmented/. and connections to known languages are shrouded in mystery, igniting fierce scientific debate among humanities scholars. Can NLP methods be helpful in bringing some clarity to these questions? Recent work has already demonstrated that algorithms can successfully decipher lost languages like Ugaritic and Linear B (Luo et al., 2019), relying only on non-parallel data in known languages—Hebrew and Ancient Greek, respectively. However, these methods are based on assumptions that are not applicable to many undeciphered scripts. The first assumption relates to the knowledge of language family of the lost language. This information enables us to identify the closest living language, which anchors the decipherment process. Moreover, the models assume significant proximity between the two languages so that a significant portion of their vocabulary is matched. The second assumption presumes that word boundaries are provided that uniquely define the vocabulary of the lost language. One of the famous counterexamples to both of these assumptions is Iberian. The Iberian scripts are undersegmented with inconsistent use of word dividers. At the same time, there is no definitive consensus on its close known language—over the years, Greek, Latin, and Basque were all considered as possibilities. In this paper, we introduce a decipherment approach that relaxes the above assumptions. The model is provided with undersegmented inscrip- tions in the lost language and the vocabulary in a known language. No assumptions are made about the proximity between the lost and the known languages and the goal is to match spans in the lost texts with known tokens. As …

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