T hamizhi UD p: A Dependency Parser for T amil (2020)

Abstract: This paper describes how we developed a neural-based dependency parser, namely ThamizhiUDp, which provides a complete pipeline for the dependency parsing of the Tamil language text using Universal Dependency formalism. We have considered the phases of the dependency parsing pipeline and identified tools and resources in each of these phases to improve the accuracy and to tackle data scarcity. ThamizhiUDp uses Stanza for tokenisation and lemmatisation, ThamizhiPOSt and ThamizhiMorph for generating Part of Speech (POS) and Morphological annotations, and uuparser with multilingual training for dependency parsing. ThamizhiPOSt is our POS tagger, which is based on the Stanza, trained with Amrita POS-tagged corpus. It is the current state-of-the-art in Tamil POS tagging with an F1 score of 93.27. Our morphological analyzer, ThamizhiMorph is a rule-based system with a very good coverage of Tamil. Our dependency parser ThamizhiUDp was trained using multilingual data. It shows a Labelled Assigned Score (LAS) of 62.39, 4 points higher than the current best achieved for Tamil dependency parsing. Therefore, we show that breaking up the dependency parsing pipeline to accommodate existing tools and resources is a viable approach for low-resource languages.

## introduction applying neural-based approaches to tamil, like other indic languages, is challenging due to a lack of quality data @xcite , and the language's structure @xcite . although there is a large volume of electronic unstructured/partially-structured text available on the internet, not many language processing tools are publicly available for even fundamental tasks like part of speech (pos) tagging or parsing. nowadays, neural-based approaches are the state of the art for most natural language processing tasks. these approaches require a significant amount of quality data for training and evaluation. on the other hand techniques like transfer learning, and multilingual learning may be used to overcome data scarcity. this paper discusses how we developed a neural-based dependency parser for tamil with the aid of data orchestration and multilingual training. ## background and motivation tamil is a southern dravidian language spoken by more than 80 million people around the world. however, it still lacks enough tools and quality annotated data to build good natural language processing (nlp) applications. ## thamizhiudp by considering all available resources and approaches as outlined in section 2, we decided to develop a universal dependency parser (udp) for tamil called thamizhiudp using existing open source tools, namely stanza, thamizhimorph, and uuparser. however, since we do not have enough data to train a neural-based parser end-to-end, we have broken up the pipeline to different phases. we have then orchestrated data from different sources for each of these phases, and used different tools in different phases, as shown in our dependency parsing pipeline has several stages as shown in figure 1 . as mentioned, we used different datasets and tools, as shown in ## discussion tamil ttb has not undergone any major revisions or corrections since its initial release. it has several issues, in pos tagging, multi-word tokenisation, and dependency tagging. altogether we only have 600 sentences for training, development, and testing; some of these sentences are very long. all these made the training of a ud parser a difficult task. we tried to overcome some of these issues using other data, and tools available online. however, we still depend on this dataset for some part of the training, such as for dependency parsing. only one treebank, telugu mtg udv2.6, which is the closest to tamil in terms of linguistic structures, is available as of today. we observed that telugu udv2.6 is small in size. that only has around 1050 sentences compared to hindi hdtb udv2.6. moreover, telugu has very short sentences without any morphological feature information. on the other hand, some sentences in ttb in udv2.6 has up to 40 tokens. because of all these varied factors we could not achieve much improvement when use telugu mtg udv2.6 in multilingual training. however, hindi, which belongs to a different language family, showed better performance when used for multilingual training. we have additionally noticed that the accuracy of the dependency parsing also improved when we increased the hindi data size during the training. another challenge we have faced was finding quality test data or benchmark datasets for evaluation. in the current practice, everyone tests their tools using their own dataset to evaluate. therefore, it is always a challenging task to reproduce or compare results. in our case, for dependency parsing, we used the ud test data. however, it is not a clean and error free dataset for evaluation. for this reason, we have now started working on a tamil dependency treebank which can soon be used as an evaluation dataset. we used our personal computers without any graphical processing units (gpu) to carry out all these experiments. however, high performance computing resources will save time, and we might need to go for such resources when we increase the size of datasets.