Larth: Dataset and Machine Translation for E truscan (2023)

Abstract: Etruscan is an ancient language spoken in Italy from the 7th century BC to the 1st century AD. There are no native speakers of the language at the present day, and its resources are scarce, as there are an estimated 12,000 known inscriptions. To the best of our knowledge, there are no publicly available Etruscan corpora for natural language processing. Therefore, we propose a dataset for machine translation from Etruscan to English, which contains 2891 translated examples from existing academic sources. Some examples are extracted manually, while others are acquired in an automatic way. Along with the dataset, we benchmark different machine translation models observing that it is possible to achieve a BLEU score of 10.1 with a small transformer model. Releasing the dataset can help enable future research on this language, similar languages or other languages with scarce resources.

## introduction etruscan (iso 639-3 code: ett) is a language spoken in the etruria region (modern-day centre italy) from the 7 th century bc to the 1 st century ad @xcite . it is written right to left using the etruscan alphabet, derived from the greek alphabet @xcite . the predominant word order in this language is mostly subject-object-verb @xcite . this pattern is similar to latin, but distinguishing it from other languages like english, where the words follows the subject-verb-object order. it has 5 cases (accusative, nominative, genitive, dative and locative), two numbers (singular and plural) and takes into consideration animacy and gender @xcite . only a small number of inscriptions in this language survived up to the present day: an estimated 12,000 inscriptions have been recovered (wallace, 1 the data and code are available here: https://github.com/gianlucavico/ larth-etruscan-nlp.git 2008). however, only a few of them have a significant length to be considered complete. other ancient languages used in similar areas and periods in history, such as latin and ancient greek, have more resources, thus, making natural language processing techniques and tools easier to develop for these languages. the contribution of this paper is threefold: first, we build a corpus of etruscan inscriptions usable for natural language processing. we use as a starting point existing academic resources for this language exist, and we try to create our corpus both by manual and automatic work. second, we focus on the machine translation task from etruscan to english. we evaluate whether neural models can be trained with this data and if they can outperform less data-hungry models. finally, we investigate if it is possible to exploit any similarity between etruscan and latin or ancient greek to improve the aforementioned model. in section 2, we introduce state-of-the-art techniques relevant to this paper. then, in sections 3 and 4 we explain the methods used to work on the data and the model used. section 5 and section 6 illustrate the experiments and compare the different techniques. finally, section 7 concludes the paper. ## literature review the etruscan texts project (etp) @xcite is a digital etruscan corpus which contains 369 inscriptions. the project is based on etruskische texte @xcite and is used in the book zihk rasna @xcite . another digital etruscan work is the corpus inscripionum etruscarum plenissimum (ciep) @xcite , based on the corpus inscriptionum etruscarum (cie) @xcite . similar works exist for latin and ancient greek, like i.phi @xcite and perseus @xcite . in addition, toolkits like cltk @xcite offer natural language pro-cessing for these languages. projects that aim to increase the resources available for low-resource languages may also include ancient languages, like the tatoeba translation challenge @xcite . it has latin and ancient greek datasets, however, it does not include etruscan. the machine translation task can be solved via neural machine translation @xcite , which involves training neural networks that take texts from the source language and generate the translation in the target language. popular architectures include long short-term memory (lstm) @xcite and transformers @xcite . these models are sequence-to-sequence @xcite , meaning they take a sequence as input and generate a sequence of possibly different lengths as output. one approach is to feed word or word pieces to the model like in t5 @xcite or @xcite . @xcite and @xcite show that it is possible to work directly on characters, while other models (shahih and purwarianti, 2019 and bansal and lobiyal, 2020) use a hybrid approach by working on both the character and word sequences. besides neural networks, other approaches include rule-based models, such as dictionary models, which translate the text based on explicit rules, and statistical models @xcite . by using the transformer architecture, ithaca @xcite is able to perform textual restoration and geographical and chronological attribution of ancient greek inscriptions. the model consists of a sparse self-attention encoder @xcite that takes as input the characters and the words of the input text, and then three feedforward blocks generate the output for each task. other examples of transformer models working on ancient languages are the multi-language translation model opus-mt (tiedemann and thottingal, 2020), tested on the @xmath0, or the language model latin-bert @xcite . translation models can be evaluated by using various metrics. @xcite proposes bleu: this metric considers the average matching precision of n-grams between the reference text and the machine-translated text. another metric is ter @xcite , which measures the quality of the translation based on the number of edits needed to change the system text to the reference one. ter and bleu are based on word n-grams, while chr-f @xcite uses the f-score of matching character n-grams. ## machine translation we compare different models for machine translation on the bleu metric but chr-f and ter metrics are also reported. the metrics are computed by sacrebleu @xcite . higher bleu and chr-f and lower ter indicate a better-performing model. moreover, we evaluate the case where we use only etp and etp+ciep for training and testing the models. ## experiments in this section, we compare different machine translation models trained on etruscan data. the models are compared on the bleu score. ## results & discussion figure 6 and figure 7 compare the scores of the models presented in the previous section. compared to the random model, the dictionary-based model shows higher bleu and chr-f scores and lower ter scores except when tested only on ciep. this suggests that ciep is noisier than etp and that the dictionary is not suited for ciep. the n-gram models perform better than random as for chr-f and ter, the dictionary model and ibm models perform better than larth. these two models can only output tokens from the training set and ignore unknown tokens. thus, they can generate longer sequences of correct characters (high chr-f) and the errors are mainly for unknown tokens or from english tokens that are not directly present in the etruscan texts like articles (low ter). whereas, larth uses tokens that can be word pieces and it still generates a translation for unknown tokens. figure 7 : comparison of the models with the best bleu scores on etp. one model from each type is selected. ## discussion in this paper, we present a dataset for @xmath1. although the dataset is not very big, we show that it is possible to train statistical and transformer models. given the unexplored nature of etruscan language, the fact that trained models perform better than random is an important first step for this language. moreoever, we demonstrated that larth performs better than the ibm models when trained on the available data. however, our model does not provide any explanation about the generated translation neither it guarantees whether it is correct. our model's performance also depends on the dataset itself, which does not contain any bibliographic information or the reasoning that the original authors used to translate the inscriptions. future work includes delivering a cleaner and more complete version of the dataset and the inclusion of additional metadata, such as bibliographic information, more accurate location, or interesting graphical information (e.g. the direction of the inscription).