The NYU - CUB oulder Systems for SIGMORPHON 2020 Task 0 and Task 2 (2020)

Abstract: We describe the NYU-CUBoulder systems for the SIGMORPHON 2020 Task 0 on typologically diverse morphological inflection and Task 2 on unsupervised morphological paradigm completion. The former consists of generating morphological inflections from a lemma and a set of morphosyntactic features describing the target form. The latter requires generating entire paradigms for a set of given lemmas from raw text alone. We model morphological inflection as a sequence-to-sequence problem, where the input is the sequence of the lemma’s characters with morphological tags, and the output is the sequence of the inflected form’s characters. First, we apply a transformer model to the task. Second, as inflected forms share most characters with the lemma, we further propose a pointer-generator transformer model to allow easy copying of input characters.

## introduction in morphologically rich languages, a word's surface form reflects syntactic and semantic properties that are expressed by the word. for example, most english nouns have both singular and plural forms (e.g., robot/robots, process/processes), which are known as the inflected forms of the noun. some languages display little inflection. in contrast, others have many inflections per base form or lemma: a polish verb has nearly 100 inflected forms @xcite and an archi verb has around 1.5 million @xcite . morphological inflection is the task of, given an input word -a lemma -together with morphosyntactic features defining the target form, gen- ## sigmorphon 2020 shared task the sigmorphon 2020 shared task is composed of three tasks: task 0 on typologically diverse morphological inflection @xcite , task 1 on multilingual grapheme-tophoneme conversion @xcite , and task 2 on unsupervised morphological paradigm completion @xcite . we submit systems to tasks 0 and 2. ## methods in this section, we introduce our models for tasks 0 and 2 and describe all approaches we use, such as multitask training, hallucination pretraining and ensembling. the code for our models is available online. foot_0 ## ablation studies our systems use three components on top of the vanilla transformer: a copy mechanism, multitask training and hallucination pretraining. we further perform an ablation study to measure the contribution of each component to the overall system performance. for this, we additionally train five different systems with different combinations of components. a description of which component is used in which system for this ablation study is shown in ## conclusion we presented the nyu-cuboulder submissions for sigmorphon 2020 task 0 and task 2. we developed morphological inflection models, based on a transformer and a new model for the task, a pointer-generator transformer, which is a transformer-analogue of a pointer-generator model. for task 0, we further added multitask training and hallucination pretraining. for task 2, we combined our inflection models with additional components from the provided baseline to obtain a fully functional system for unsupervised morphological paradigm completion. we performed an ablation study to examine the effects of all components of our inflection system. finally, we designed a low-resource experiment to show that using the copy mechanism on top of the vanilla transformer is beneficial if training sets are small, and achieved results close to a stateof-the-art model for low-resource morphological inflection.