Explicit linguistic knowledge, encoded by resources such as rule-based morphological analyzers, continues to prove useful in downstream NLP tasks, especially for low-resource languages and dialects.Rules are an important asset in descriptive linguistic grammars.However, creating such resources is usually expensive and non-trivial, especially for spoken varieties with no written standard.In this work, we present a novel approach for automatically learning morphophonological rules of Arabic from a corpus.Motivated by classic cognitive models for rule learning, rules are generalized cautiously.Rules that are memorized for individual items are only allowed to generalize to unseen forms if they are sufficiently reliable in the training data.The learned rules are further examined to ensure that they capture true linguistic phenomena described by domain experts.We also investigate the learnability of rules in low-resource settings across different experimental setups and dialects

Morphological inflection is a popular task in sub-word NLP with both practical and cognitive applications.For years now, state-of-theart systems have reported high, but also highly variable, performance across data sets and languages.We investigate the causes of this high performance and high variability; we find several aspects of data set creation and evaluation which systematically inflate performance and obfuscate differences between languages.To improve generalizability and reliability of results, we propose new data sampling and evaluation strategies that better reflect likely usecases.Using these new strategies, we make new observations on the generalization abilities of current inflection systems.

Modern work on the cross-linguistic computational modeling of morphological inflection has typically employed language-independent data splitting algorithms.In this paper, we supplement that approach with language-specific probes designed to test aspects of morphological generalization.Testing these probes on three morphologically distinct languages, English, Spanish, and Swahili, we find evidence that three leading morphological inflection systems employ distinct generalization strategies over conjugational classes and feature sets on both orthographic and phonologically transcribed inputs.