Out-of-Domain Dependency Parsing for Dialects of A rabic: A Case Study (2024)

Abstract: We study dependency parsing for four Arabic dialects (Gulf, Levantine, Egyptian, and Maghrebi). Since no syntactically annotated data exist for Arabic dialects, we train the parser on a Modern Standard Arabic (MSA) corpus, which creates an out-of-domain setting.We investigate methods to close the gap between the source (MSA) and target data (dialects), e.g., by training on syntactically similar sentences to the test data. For testing, we manually annotate a small data set from a dialectal corpus. We focus on parsing two linguistic phenomena, which are difficult to parse: Idafa and coordination. We find that we can improve results by adding in-domain MSA data while adding dialectal embeddings only results in minor improvements.

## introduction syntactic parsing for arabic is challenging, mostly because of its morphological complexity. parsing dialectal arabic is more complex due in part to the lack of syntactically annotated corpora. consequently, this task has not been as extensively studied. the available treebanks for arabic, such as catib @xcite , camel treebank @xcite and patb @xcite , are all in msa. msa is used for formal written communications and in news, while dialectal arabic (da) is used for all other communication forms. because of the lack of dialectal treebanks, the most common scenario is training on msa and testing on dialectal data, without quantitative means of assessing performance. training on msa treebanks does not only mean differences in dialects, but also in text domains, further complicating the issue. in such scenario, the domain shifts result in a drop in performance due to differences in properties of the data (e.g., @xcite . we group both types of differences under domain * the work was done prior to joining amazon. given that syntactically annotating a treebank for dialectal arabic requires an extensive amount of manual work, we decided to focus on two syntactic constructions, which are known to be difficult to parse and that show significant differences across the dialects: idafa and coordination relations @xcite . 1 we assume that if we can improve parsing quality for these difficult constructions, parsing quality overall should also increase, especially since most of our modifications to handle the out-of-domain scenario are not specifically geared towards handling these phenomena but are rather general. we investigate methods to improve dependency parsing of dialectal arabic in an out-of-domain setting, when there is no available training data that matches the test data in both domain and dialect. we experiment with a selection of similarity criteria to adapt our source to the target data to improve performance. for evaluation, we annotate test sentences for four dialects of arabic, covering the phenomena listed above. ## error analysis we had a closer look at the idafa results based on the training set without sentential coordination. we 8 for the exact percentages, see ## conclusion and future work in this paper we investigated the question of dependency parsing for dialects of arabic when using out-of-domain data for training. since no syntactically annotated data exist for dialectal arabic, we have annotated a small test set, focusing on two difficult to parse phenomena, idafa and coordination. we investigated methods for making the training data more similar to the test data and the effect of using dialectal embeddings in the parser. results show that we reach the highest results by adding msa data from the same domain. thus, we conclude that the text genre differences have more of a negative effect on the parser than dialectal differences. for future work, we plan to extend this study to more syntactic phenomena, and we will investigate more traditional domain adaptation methods.