H ei M orph at SIGMORPHON 2022 Shared Task on Morphological Acquisition Trajectories (2022)

Abstract: This paper presents the submission by the HeiMorph team to the SIGMORPHON 2022 task 2 of Morphological Acquisition Trajectories. Across all experimental conditions, we have found no evidence for the so-called Ushaped development trajectory. Our submitted systems achieve an average test accuracies of 55.5% on Arabic, 67% on German and 73.38% on English. We found that, bigram hallucination provides better inferences only for English and Arabic and only when the number of hallucinations remains low.

## introduction morphological inflection concerns generating the inflected word form given the lemma and a set of morphosyntactic descriptions. a morphology learner (human or machine) must be able to generalise patterns from extremely sparse data. observations from morphology acquisition by children provides us with a glimpse of how learners generalise regular and irregular patterns differently and how the trajectories of pattern generalisations interact with a small but growing lexicon. this paper describes our approach and results for task 0 part 2 of the sigmorphon 2022 shared task on morphological acquisition trajectories. two main challenges of the task are that it covers two different inflectional patterns (past tense and noun plurals) over three languages and that there is only a small amount of training data ranging from as few as 100 samples to as many as 1,000 samples. this extreme data sparsity calls for the use of data hallucination techniques commonly used for lowresourced nlp development @xcite . the neural baseline provided by the shared task is based on input-variant transformer @xcite or the vanilla transformer with optional data augmentation @xcite . we use a multi-headed self-attention transformer with unigram-aware and bigram-aware data hallucinations. our models yielded an improved average test accuracy by 2.66% on arabic, 8.69% on german, 4.5% on english, as compared with the neural baseline results. ## background and data the details of the task description can be found at https://github.com/sigmorphon/ 2022inflectionst . we use the data provided by the sigmorphon 2022 shared task (part 2) @xcite . the data features lemmas, inflections, and corresponding morphosyntactic description (msd) using the uni-morph schema @xcite . the data was released for english, german and arabic. the specific inflectional patterns were the english past tense @xcite , german noun plurals @xcite and arabic noun plurals (dawdy-hesterberg and pierrehumbert, 2014). ## system description in this section, we describe the neural network architecture, the data hallucination process and the submissions. ## results ## conclusion how do children learn morphology? it has often been noted that children start out using correct forms, followed by a period of regularlizing irregular forms, followed by mastery of the morphology @xcite -often called the u-shaped development. this development can be found in arabic @xcite @xcite , german @xcite and english @xcite . in our simulations we have found no evidence for such a development. this is, in fact, a good thing. assuming a u-shaped development in morphological acquisition is too coarse, as the literature says little if anything about the question whether the (very few) forms used by very young children are used in the correct morphosynatctic environment. moreover, this literature assumes that learning morphology involves learning how forms map onto other forms, reminiscent of the paradigm cell filling problem @xcite @xcite for example, how does a singular form map onto a plural form? the role of meaning is very limited, often not more than a contrastive label. however, the fact that children gradually reduce the number of overgeneralizations of irregular forms can be explained by the way in which children learn which word forms are used to express which particular meanings @xcite . our experiment with restricting the hallucination process to generate forms that are phonotactically attested (bigram) in the training data revealed that its benefit was found only in very restricted conditions depending on the amount of hallucinated samples and the specific language (and presumably the inflectional pattern). our findings are in agreement with the detailed error analyses of data hallucination techniques by @xcite which concluded that hallucination is not a one-size-fits-all technique and it must be used with caution and requires closer inspection depending on the type of morphological inflections.