Investigation of Transfer Languages for Parsing L atin: Italic Branch vs. H ellenic Branch (2021)

Abstract: Choosing a transfer language is a crucial step in transfer learning. In much previous research on dependency parsing, related languages have successfully been used. However, when parsing Latin, it has been suggested that languages such as ancient Greek could be helpful. In this work we parse Latin in a low-resource scenario, with the main goal to investigate if Greek languages are more helpful for parsing Latin than related Italic languages, and show that this is indeed the case. We further investigate the influence of other factors including training set size and content as well as linguistic distances. We find that one explanatory factor seems to be the syntactic similarity between Latin and Ancient Greek. The influence of genres or shared annotation projects seems to have a smaller impact.

## introduction there have been multiple projects exploiting the benefits of multilingual dependency parsing 1 @xcite and especially the use of transfer learning in low-resource scenarios @xcite . transfer learning in the context of parsing lowresource languages uses knowledge from a transfer language in order to parse the low-resource target language @xcite . determining the optimal transfer language for any target language is a crucial step usually leading to the selection of a language that belongs to the same language family as the target language @xcite @xcite . however, language proximity is not always the best criterion, since there are other properties that 1 often mentioned as cross-lingual dependency parsing could lead to better results such as the content of the syntactic, geographical, or phonological distances, which is confirmed by studies both in machine translation @xcite and syntactic parsing @xcite . @xcite noted that for latin, it was useful to group it with other ancient languages such as ancient greek and gothic, but they did not provide a comparison with other potential transfer languages. we perform an investigation of parsing latin in a low-resource setting, with the goal of investigating if greek languages are better as transfer languages than italic languages. we also explore the role of factors such as treebank size, treebank content and linguistic distance measures. we find that ancient greek, and also modern greek, are indeed a better choice as transfer languages for latin than the related italic languages italian and french. we further show that while using ancient greek data from the same annotation project is preferable, it is not the sole cause of the strong results, since good results are had also across different annotation projects. these results also hold for different training data sizes. finally we note that ancient greek is syntactically more similar to latin than italian, which can be an explanatory factor. ## related work multilingual parsing has been an active topic of research over the last decade, but there is a limited number of studies that focus on transfer language selection. there are works that include language selection techniques for dependency parsing such as using a typological database to choose transfer languages based on their typological weight similarities to the target language @xcite . similarly, @xcite use a partof-speech sequence similarity method between the source and target language. a more detailed investigation on transfer language selection is performed by @xcite . they attempt to build models that rank languages based on linguistic distance measures in order to predict the optimal transfer languages. another option is to choose the most suitable single-source parser among a set of parsers, either at the level of language @xcite or for individual sentences @xcite , often based on part-ofspeech patterns. ## experimental setup our main aim is to investigate the impact of different transfer languages on low-resource latin parsing. in addition we explore the impact of training data size and content, as well as the connection to a number of distance measures between languages. ## conclusion we have shown that using hellenic languages is preferable to using italic languages when training a multilingual parsing model for latin in a low resource scenario. while we see the best results when we use ancient greek treebanks from the same annotation project as the latin treebanks, we also see very competitive results when training across annotation projects, mostly surpassing all other languages explored. we also see that it is more useful to increase the training data size of the target language than the transfer language, and that increasing the size of the target language is only useful when it is a good match. finally we show that there are strong correlations between the parsing result and the featural and syntactic dis-tance of the target and transfer language, which could explain the usefulness of ancient greek, the most syntactically similar language to latin in our sample. in this study we only explored a low-resource setting, using a limited amount of latin data. it would be interesting to see if the findings hold also when we use all available data, as indicated by the results of @xcite . we would also like to add pre-trained word embeddings, either crosslingual static embeddings, or multilingual contextual embeddings, to see what the impact is, compared to our current experiments where we do not use any pre-trained embeddings. another direction would be to investigate if ancient greek is a good transfer language for latin also for other tasks, which might be less sensitive to syntactic distance.