We focus on morphological inflection in out-of-vocabulary (OOV) conditions,
an under-researched subtask in which state-of-the-art systems usually are less
effective. We developed three systems: a retrograde model and two
sequence-to-sequence (seq2seq) models based on LSTM and Transformer. For
testing in OOV conditions, we automatically extracted a large dataset of nouns
in the morphologically rich Czech language, with lemma-disjoint data splits,
and we further manually annotated a real-world OOV dataset of neologisms. In
the standard OOV conditions, Transformer achieves the best results, with
increasing performance in ensemble with LSTM, the retrograde model and
SIGMORPHON baselines. On the real-world OOV dataset of neologisms, the
retrograde model outperforms all neural models. Finally, our seq2seq models
achieve state-of-the-art results in 9 out of 16 languages from SIGMORPHON 2022
shared task data in the OOV evaluation (feature overlap) in the large data
condition. We release the Czech OOV Inflection Dataset for rigorous evaluation
in OOV conditions. Further, we release the inflection system with the seq2seq
models as a ready-to-use Python library.

我们的研究聚焦于词形屈折在词汇外条件下的问题，通过开发三个系统并测试在词汇外数据集上的性能，我们发现逆行模型在现实情境下的词汇外数据上胜过了所有神经模型，并且由我们的 seq2seq 模型在 SIGMORPHON 2022 共享任务数据的大数据条件下达到了最先进的结果。