Learning Cross-Dialectal Morphophonology with Syllable Structure Constraints (2025)

Abstract: We investigate learning surface forms from underlying morphological forms for low-resource language varieties. We concentrate on learning explicit rules with the aid of learned syllable structure constraints, which outperforms neural methods on this small data task and provides interpretable output. Evaluating across one relatively high-resource and two related low-resource Arabic dialects, we find that a model trained only on the high-resource dialect achieves decent performance on the low-resource dialects, useful when no low-resource training data is available. The best results are obtained when our system is trained only on the low-resource dialect data without augmentation from the related higher-resource dialect. We discuss the impact of syllable structure constraints and the strengths and weaknesses of data augmentation and transfer learning from a related dialect.

## introduction many of the world's under-resourced language varieties are closely related to higher-resourced varieties. this suggests two possibilities for progress on the under-resourced varieties: the development of systems that perform better with smaller training data, and the development of systems that leverage information from the higher-resource variety to augment learning for the lower-resource one. in this paper, we combine these two approaches: we employ a learning technique that works well with small amounts of data (namely, rule learning) and we evaluate the impact of providing the the model training data combined from both a low-resourced variety and a similar but higher-resourced variety. arabic is particularly well-suited for studying such techniques because the arabic dialects represent a continuum of related but distinct and thriving spoken varieties, yet most have limited computational resources available for them. hand, the dialects maintain varying degrees of mutual intelligibility. a system developed for one dialect will may not capture everything in another dialect, but they are generally close enough that some transfer learning should be feasible. furthermore, arabic is morphologically rich. even affixation in arabic triggers a range of morphophonological processes which may yield surface forms that are noticeably different from their underlying morphological analyses. uncovering these processes is crucial for understanding arabic morphology. moreover, difference in these processes account for much of the difference between the spoken forms across dialects. underlyingly identical forms across dialects may surface very differently, as examples show in in this paper, we study how we can use resources for this relatively resource-rich dialect and apply them to resource-poor dialects. we take on the task of matching annotated underlying forms to attested surface forms @xcite . @xcite study this task for cairene egyptian arabic (egy), which, while not high-resourced in absolute terms, has quite a few scholarly and corpus resources available, much more than other dialects. they show that when only small amounts of data are available, rule-learning approaches outperform neural sequence-to-sequence models. they also perform a somewhat perfunctory study on sudanese arabic (sud). we adopt their general problem formulation, but we specifically investigate how we can apply egy resources to other dialects, choosing for our study sud and jordanian (jor), two low-resource dialects. we investigate a variety of techniques on these two dialects which are relatively close to cairene, but differ in their details. we investigate four training conditions, which combine transcribed spoken arabic dialect data in different ways. in our experience, while not naturally occurring, some transcribed speech is available for many arabic dialects, and it is more easily obtained than the underlying representations. the conditions are a follows. for clarification, "full data" refers to a corpus of pairs of underlying morphological representations and surface forms, while "surface forms" only refers to a corpus which contains attested forms (transcribed spoken language), but no linguistic analysis has been performed to create the underlying representations: our paper makes two primary contributions: the structure of this paper is as follows. we discuss related work in section 2. we present the linguistic background in section 3 and the data in section 4. we present our method with details on all steps in section 5, and report on experimental results in section 6. we conclude with an analysis and a report on ongoing and future work. ## data in order to learn morphophonology mappings, the data is represented in pairs of underlying representations (ur) which is a sequence of morphs in a hypothetical but consistent form that could be motivated theoretically or derived from the output of a morphological analyzer, and a surface (spoken) form (sf), which is phonically transcribed. the ldc transcription scheme was used for both ur and sf. a mapping between ldc, ipa, and arabic script can be found in appendix we augmented the character set with a symbol for word boundaries #, a symbol for prefix boundaries -, and a symbol for suffix boundaries =. we opted for only open class words, i.e., nouns, adjectives, and verbs, as other categories such as proper nouns are more likely to manifest exceptional processes which violate the otherwise norms in their respective dialects. in addition, we restrict learning to concatenative morphology. we leave templatic morphology for future studies. the major consequence of this design decision is that different templatic realizations within a given morphological egy jor sud ur sf ur sf ur sf ti-kallif tikallif ti-kallif 'itkallif ta-kallif takallif #cv cvc cvc# #cvc cvc cvc# #cv cvc cvc# bi-ti-kallif bitkallif bi-ti-kallif bitkallif bi-ta-kallif bitkallif #cvc cvc cvc# #cvc cvc cvc# #cvc cvc cvc# @xmath3. ## methodology and experimental setup our approach extends the pruned abundance rule learning algorithm (parla; @xcite as the primary rule learning technique; our contributions lie mainly in exploring several aspects of cross-dialectal learning. our research focus is on new data augmentation techniques for dialect transfer, improved rule learning scope, and the inclusion of syllables structure as a linguistically motivated signal for rule learning. ## evaluation we organize the evaluation discussion according to our data setups introduced in section 5. ## conclusion and future work in this work we investigated cross-dialectal learning of morphophonology of three arabic dialects -egyptian, sudanese, and jordanian -through rule learning, where we generate a spoken form from an underlying morphological representation. we explored different scenarios of data availability where egyptian is taken to be the rich-resource dialect while sudanese and jordanian are under-resourced. we found that training on the under-resourced dialect alone outperformed transfer from the higherresourced dialect, alone or in combination with the under-resourced dialect. furthermore, we introduced learned syllable structure properties as an additional linguistic well-formedness measure, which nearly always boosted performance, particularly when used in the absence of training data from the under-resource dialect. some of the analyses suggest that cross-dialectal learning using high resource data that is potentially contradictory with the target dialect is needed. po-tential techniques we plan to explore involve reinforcement learning and active learning. we additionally plan on carrying more careful analysis of the rules and how they compare across the dialects. we will also explore incorporating more linguistic signals such as stress assignment since it is closely tied with some phonological processes. additionally, we are working on investigating more dialects across the continuum as more data become available. finally, we plan to investigate ways to unify underlying representations in reasonable ways to allow a clearer classification of the rule types across dialects.