Abstract
We present a model for the unsupervised dis- covery of morphological paradigms. The goal of this model is to induce morphological paradigms from the bible (raw text) and a list of lemmas. We have created a model that splits each lemma in a stem and a suffix, and then we try to create a plausible suffix list by con- sidering lemma pairs. Our model was not able to outperform the official baseline, and there is still room for improvement, but we believe that the ideas presented here are worth considering.
Highlights
In this paper we describe our attempt to capture morphological paradigms totally from scratch (Kann et al, 2020) prepared for the task of morphological paradigm completion in the CoNLL–SIGMORPHON 2020 Shared Task
For determining rm we identify the inflections of the lemmas with regular inflection
Our results are behind the official baseline, and there is a wide range of possibilities for improvement
Summary
In this paper we describe our attempt to capture morphological paradigms totally from scratch (Kann et al, 2020) prepared for the task of morphological paradigm completion in the CoNLL–SIGMORPHON 2020 Shared Task. Computational morphology is not a new area and there is plenty of related work Some years ago, this problem was commonly tackled using finite-state and two-level approaches, such as in Kaplan and Kay (1994), Beesley and Karttunen (2003), and Koskenniemi (1983). There have been several Shared Tasks recently on morphological inflection (Cotterell et al, 2016, 2017, 2018; McCarthy et al, 2019) The task for this year is more complex, as we are asked to discover paradigms from scratch. This is an intriguing research area that could give us the chance of recovering dead languages that have only limited written resources. Several researchers have attempted to solve this task, such as Goldsmith et al (2017), Jin et al (2020), and Erdmann et al (2020)
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