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        <title>Generalizing Morphological Inflection Systems to Unseen Lemmas</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Generalizing Morphological Inflection Systems to Unseen Lemmas
%A Yang, Changbing
%A Yang, Ruixin (Ray)
%A Nicolai, Garrett
%A Silfverberg, Miikka
%Y Nicolai, Garrett
%Y Chodroff, Eleanor
%S Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F yang-etal-2022-generalizing
%X 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.
%R 10.18653/v1/2022.sigmorphon-1.23
%U https://aclanthology.org/2022.sigmorphon-1.23
%U https://doi.org/10.18653/v1/2022.sigmorphon-1.23
%P 226-235

Markdown (Informal)

[Generalizing Morphological Inflection Systems to Unseen Lemmas](https://aclanthology.org/2022.sigmorphon-1.23) (Yang et al., SIGMORPHON 2022)

• Generalizing Morphological Inflection Systems to Unseen Lemmas (Yang et al., SIGMORPHON 2022)

ACL

• Changbing Yang, Ruixin (Ray) Yang, Garrett Nicolai, and Miikka Silfverberg. 2022. Generalizing Morphological Inflection Systems to Unseen Lemmas. In Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 226–235, Seattle, Washington. Association for Computational Linguistics.