C amel P arser2.0: A State-of-the-Art Dependency Parser for A rabic (2023)

Abstract: We present CamelParser2.0, an open-source Python-based Arabic dependency parser targeting two popular Arabic dependency formalisms, the Columbia Arabic Treebank (CATiB), and Universal Dependencies (UD). The CamelParser2.0 pipeline handles the processing of raw text and produces tokenization, part-of-speech and rich morphological features. As part of developing CamelParser2.0, we explore many system design hyper-parameters, such as parsing model architecture and pretrained language model selection, achieving new state-of-the-art performance across diverse Arabic genres under gold and predicted tokenization settings.

## introduction dependency parsing is a natural language processing (nlp) task used to analyze the grammatical structure of a sentence by identifying and representing the relationships between its words. dependency parsing assigns a directed tree structure to the sentence, with words as nodes and syntactic dependencies as edges (see figure 1 ). dependency parsing, and syntactic parsing in general, has long been considered an important nlp enabling technology and analysis tool @xcite . the interest in using syntactic structures in nlp in the neural age remains, e.g., as analytical tools for studying large language models @xcite , for guided data augmentation for neural machine translation @xcite , semantic role labeling @xcite , and grammatical error correction @xcite . there have been previous developments in arabic dependency parsing @xcite @xcite @xcite . however, they are not based on state-of-the-art (sota) developments in neural dependency parsing and pre- 'w+' 'hl' 's+' 'yšrhwn' '+ha' '?' ‫وھﻞ‬ nha ? figure 1 : an example catib dependency tree @xcite for the short question whl syšrhwnha? 2 'and will they explain it?'. in this work, we investigate the effect of many system design hyper-parameters including parsing model architecture, pretrained language model selection, and training data configurations to achieve unprecedented dependency parsing performance on multiple arabic genres. hence, we present camelparser2.0, an open-source dependency parsing pipeline that achieves sota performance on columbia arabic treebank (catib) and universal dependencies (ud) parsing of arabic across multiple genres from modern standard arabic (msa) and classical arabic (ca). our contributions are: (1) achieving new stateof-the-art on both catib and ud formalisms in multiple arabic genres on all metrics; (2) developing and releasing an open-source pythonbased pipeline for arabic parsing; 1 and (3) benchmarking a large number of hyper-parameters to ensure the best system design choices. ## the camelparser2.0 pipeline in this section, we present the details of the camel-parser2.0 pipeline (figure 2 ). the pipeline accommodates varying levels of pre-processing in the input. depending on the extent to which the input has been pre-processed, the pipeline conducts morphological disambiguation. once input tokens have been identified, they are passed to the dependency parsing system which outputs dependency arcs and labels. the dependency relations are then combined with the form and additional part-ofspeech tags and morphological features, which are either specified in the input or generated in the morphological disambiguation step, to output a conll-x/conll-u file format @xcite . ## experimental setup our experimental setup involves training multiple dependency parsing models with different training data configurations which are then evaluated on multi-genre development and test sets under both gold and predicted tokenization settings to gauge accuracy and robustness across multiple genres in arabic. the details of the various experimental setups are outlined below. ## results and analysis we present the results of the experiments we conducted as part of developing camelparser2.0. ## conclusion and future work we presented camelparser2.0, a new sota opensource, python-based arabic dependency parser that supports ud and catib formalisms and multiple arabic genres. we make camelparser2.0 publicly available. 7 in the future, we plan to continue to enhance the camelparser2.0 models and integrate them in downstream applications to support arabic nlp. we also plan to extend the parser to cover multiple arabic dialects.