First Steps towards U niversal D ependencies for L az (2020)

Abstract: This paper presents the first treebank for the Laz language, which is also the first Universal Dependencies Treebank for a South Caucasian language. This treebank aims to create a syntactically and morphologically annotated resource for further research. We also aim to document an endangered language in a systematic fashion within an inherently cross-linguistic framework: the Universal Dependencies Project (UD). As of now, our treebank consists of 576 sentences and 2,306 tokens annotated in light with the UD guidelines. We evaluated the treebank on the dependency parsing task using a pretrained multilingual parsing model, and the results are comparable with other low-resourced treebanks with no training set. We aim to expand our treebank in the near future to include 1,500 sentences. The bigger goal for our project is to create a set of treebanks for minority languages in Anatolia.

## introduction in recent years, many understudied languages have been in the spotlight of nlp studies. within the universal dependencies framework @xcite , languages like wolof (dione, 2019), mbyá guaraní @xcite , eryza @xcite , bhojpuri (ojha and zeman, 2020), and many others have been introduced to nlp studies. laz is also another understudied language on which there are no nlp resources, with the exception of a recent morphological analyzer @xcite . laz is spoken in the southeastern part of the black sea among a declining population which is estimated to consist of somewhere between 250,000 and 500,000 people @xcite . it is a highly agglutinative language which makes use of prefixes and suffixes, and it is reported to have 16 different slots for verb inflection @xcite . laz is also reported to have extensive dialectal variation @xcite . in this work, when we use the word laz, we specifically mean the atina-pazar dialect of laz. through this work, we hope to contribute to the revitalization efforts of the laz language by providing a gold standard dependency treebank. we provide the first publicly available human-annotated morphosyntactic laz treebank. we utilize the already existing universal dependency framework to represent upos tags, morphological features, and syntactic dependency relations. we also provide parsing results with no training data using udify @xcite . thus, we provide attachment scores (uas and las) which are comparable with other low resource languages within the ud framework. ## linguistic analysis of laz dependency treebank as we stated earlier, we followed ud v2 guidelines to annotate our laz treebank. there were some challenging data that needed to be implemented to ud framework. we mention three of them in this section with the linguistic discussion behind our decision. ## parsing with a multilingual parser since the size of the treebank is insufficient to train a dependency parser for the laz language, we instead observed the parsing success of a multilingual parser on our treebank without using any resources on the laz language in the training phase. we chose to use udify for this task. udify is a state-of-the-art multilingual multi-task model that can predict annotations for any treebank annotated in ud style. the udify model is fine-tuned on multilingual bert pretrained embeddings @xcite and can syntactically annotate sentences in any language without requiring any language-specific components. we want to benefit from an automatic annotator in the hope that it will ease the manual annotation task of additional laz text. we used a pretrained multilingual udify model to parse our treebank. since the laz language does not have any nlp resources other than this treebank, such as pretrained word embeddings or a publicly available corpus, foot_4 the language is completely unknown to the udify model. moreover, none of the language resources that were used in training of the udify and bert models belong to south caucasian language family, which includes the laz language. ## conclusion this paper reports the development of the first laz treebank ever. considering that there is no laz corpus or treebank that precedes this work and no treebank from the south caucasian language family, we believe our work will be an important contribution to the field and support the further development of typological studies that utilize the ud framework. in addition, our work is also a gold standard for both syntactic and morphological annotation which will help many future studies on both laz and georgian. our treebank currently consists of 576 sentences extracted from linguistic research. this paper discusses three topics of laz grammar which can contribute to the ud framework in the future. additionally, we report dependency parsing results with a multilingual parser: udify. the parsing accuracy of our treebank in a zero-shot learning setting are found to be similar to other low-resourced treebanks with no training data. the main aim of our work is to create treebanks for the minority languages spoken in anatolia, including cappadocian greek, pontic greek, pomak, ladino, and many others. we believe that these series of treebanks can promote future research in language contact studies as well as nlp studies in these languages. additionally, we hope to contribute to the revitalization efforts for these languages, including laz. in the near future, we will expand our treebank to include 1,500 sentences before annotating any other language.