Towards Learning A rabic Morphophonology (2022)

Abstract: One core challenge facing morphological inflection systems is capturing language-specific morphophonological changes. This is particularly true of languages like Arabic which are morphologically complex. In this paper, we learn explicit morphophonological rules from morphologically annotated Egyptian Arabic and corresponding surface forms. These rules are human-interpretable, capture known morphophonological phenomena in the language, and are generalizable to unseen forms.

## introduction much progress has been made in tasks such as morphological (re-)inflection and morphological analysis in recent years (e.g., @xcite @xcite . however, low-resource languages still prove to be a significant challenge, despite growing interest, as the recent sigmorphon shared task reveals @xcite . arabic dialects present a specific challenge in that there is an almost continual variation between dialects, mainly along the geographical dimension, and most dialects are low-resource. cairene arabic morphology is related to that of the dialects of (for example) alexandria, sohag, aswan, and khartoum. so from an nlp point of view, if we have knowledge of cairene and need a morphological tool for one of the other dialects, we should be able to leverage our knowledge of cairene. we propose to address the challenge by modeling morphophonological rules explicitly. such rules provide an explainable representation of morphophonology. once we have those rules, we can create nlp tools while leveraging rules from adjacent dialects. having a standalone model of morphology, in terms of morphophonology or morphosyntax, improves the performance of many downstream nlp tasks such as machine translation @xcite @xcite , speech synthesis @xcite and morphological disambiguation @xcite . morphological resources provide explicit linguistic knowledge that is not necessarily captured by learning models. in this paper we present a preliminary study on automatically learning morphophonological rules for cairene egyptian arabic (henceforth, egy). we choose egy because it is well-studied and has many resources. the learning process relies on the rule representation inspired by the notion of phonological rules, where a phonological alternation is explicitly represented via an input, an output, and the phonemic context. we evaluate our approach based the accuracy of the generated forms and the genralizability of the learned rules. additionally, we describe the dataset preparation process as there is no suitable dataset for our task. to the best of our knowledge, this task of rule-learning to specifically model morphophonology has not been studied before in the context of arabic nlp. this study will help us investigate to what degree can we learn explicit linguistic properties from simple representations. ## related work there have been many efforts on morphological modeling for arabic. precompiled tabular morphological analyzers @xcite @xcite @xcite became the standard in many arabic nlp pipelines. while they provide rich morphological analysis, they are directly encoded into the lexicon and do not explicitly model descriptive linguistic phenomena such as morphophonological interactions. in contrast, earlier efforts that modeled morphology using finitestate technology (e.g., @xcite used explicit rules leveraging roots and patterns. however, they were manually built and were abstracted to a high degree. more re- dialect realization egyptian kitabha sudanese kitaaba hijazi kitaabaha emirati kitaabha however, the rules are manually created and do not model phonological representations. other efforts adopting neural approaches to modeling morphological inflections @xcite @xcite perform well for many languages, however, those models do not provide insightful general rules or descriptions of linguistic phenomena. in this effort we take a generative view on morphophonology and we aim to learn morphophonological rules and apply them automatically. rule representation the transformation rules that we aim to extract are inspired by the sound pattern of english (spe; chomsky and halle, 1968), where a hypothetical underlying representation (ur) is transformed into a surface form (sf) by the application of a series of rules. below is an example of a phonological rule representing rdropping in many dialects of british english, where r is dropped when it falls between a vowel and a syllable boundary our work takes inspiration from the main three components of a rule, which are the ur, sf, and the context. the exact notion of rule, however, differs in order to make it machine-friendly. to this end, we take additional inspiration from two-level phonology @xcite , which compresses stacks of spe rules into a single ur and sf without intermediate steps. ## data our focus is on developing an explainable learning approach. therefore, we control our experimental setup by having a few assumptions: a) we deal with whole words out of context, b) the data is in a broad phonetic transcription, c) sf is the word produced and ur is the morphologically segmented underlying representation, and d) the phonemic and morphemic inventories are assumed to be acquired beforehand (for example, by observing words in which the segmentation task is trivial). though egy is resource-rich relative to many other varieties, there is no dataset that has been annotated for the task of morphophonological learning. to build such a dataset we need to create pairs of ur and sf to learn and evaluate morphophonological rules. in this work, we employ two existing resources created specifically for egy: ecal, and calima egy . ## learning approach we frame the learning problem as learning simple transformation rules that capture morphophonological interactions in a given dataset of (ur,sf) pairs. ## evaluation to evaluate our current rule learning approach, we compute the accuracy of the generated sf for every ur, reported in ## discussion and error analysis the results discussed in §6 are good indicators of the generalizability of all the components of our approach, including rule representation, extraction, and selection heuristics. we performed a qualitative error analysis to further verify the generalizability and linguistic validity of the acquired rules. ## conclusion and future work in this paper we presented a morphophonological learning approach for egyptian arabic. the main goal was to learn morphophonological rules from pairs of underlying representations and surface forms. we achieved this goal with a production accuracy of 82% on the evaluation set and 68% on completely unseen tokens from the same set. additionally, the linguistic phenomena captured through the rules align with the descriptive grammars of egyptian arabic. this effort also resulted in a new dataset designed specifically for this task. the dataset was generated by combining relevant information from a pronunciation lexicon and an orthography-based morphological analyzer. in ongoing work, we continue to develop the crucial components of our rule learning approach. we are focusing on developing a more dynamic approach to determine the context of a change and the degree of phone abstraction. we will validate our approach by applying it to more dialects, including dialects with very scarce resources. additionally, in low resource simulated settings, we plan to investigate the cognitive plausibility of the rules which will give insights to child acquisition of morphophonological phenomena.