The NYU-CUBoulder Systems for SIGMORPHON 2020 Task 0 and Task 2

Autor:	Singer, Assaf, Kann, Katharina
Rok vydání:	2020
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning I.2.7 I.2.6
Druh dokumentu:	Working Paper
Popis:	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. Our best performing system for Task 0 is placed 6th out of 23 systems. We further use our inflection systems as subcomponents of approaches for Task 2. Our best performing system for Task 2 is the 2nd best out of 7 submissions. Comment: 8 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2006.11830 Zobrazit plný text záznamu View this record from Arxiv