Generalizing Morphological Inflection Systems to Unseen Lemmas
Changbing Yang, Ruixin Yang, Garrett Nicolai, Miikka Silfverberg · 2022
This paper presents experiments on morphological inflection using data from the SIGMORPHON-UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection.We present a transformer inflection system, which enriches the standard transformer architecture with reverse positional encoding and type embeddings.We further apply data hallucination and lemma copying to augment training data.We train models using a two-stage procedure: (1) We first train on the augmented training data using standard backpropagation and teacher forcing.(2) We then continue training with a variant of the scheduled sampling algorithm dubbed student forcing.Our system delivers competitive performance under the small and large data conditions on the shared task datasets.