Can artificial neural networks learn to represent inflectional morphology and
generalize to new words as human speakers do? Kirov and Cotterell (2018) argue
that the answer is yes: modern Encoder-Decoder (ED) architectures learn
human-like behavior when inflecting English verbs, such as extending the
regular past tense form -(e)d to novel words. However, their work does not
address the criticism raised by Marcus et al. (1995): that neural models may
learn to extend not the regular, but the most frequent class -- and thus fail
on tasks like German number inflection, where infrequent suffixes like -s can
still be productively generalized.
To investigate this question, we first collect a new dataset from German
speakers (production and ratings of plural forms for novel nouns) that is
designed to avoid sources of information unavailable to the ED model. The
speaker data show high variability, and two suffixes evince 'regular' behavior,
appearing more often with phonologically atypical inputs. Encoder-decoder
models do generalize the most frequently produced plural class, but do not show
human-like variability or 'regular' extension of these other plural markers. We
conclude that modern neural models may still struggle with minority-class
generalization.

通过研究人类和 Encoder-Decoder 模型在英语和德语动词词形和名词复数形式的记忆和推广的行为，我们发现现代神经模型可能仍然难以适应少数种类的推广。