当没有多数时的变形：编码器-解码器神经网络作为德语复数认知模型的局限性
Inflecting when there's no majority: Limitations of encoder-decoder neural networks as cognitive models for German plurals
摘要：
人工神经网络能像人类说话者那样学会表示屈折形态并概括为新词吗？Kirov和Cotterell（2018）认为答案是肯定的：现代编码器-解码器（ED）架构在改变英语动词时学习类似人类的行为，例如将常规的过去时态形式-（e）d扩展到新单词。然而，他们的工作并没有解决Marcus et al.（1995）提出的批评：神经模型可能会学习扩展不是常规的，而是最常见的类-因此在德国数字变形等任务上失败，其中不常见的后缀如-s仍然可以有效地推广。为了研究这个问题，我们首先收集了一个新的数据集从德语（生产和评级的复数形式的新名词），旨在避免信息来源不可用的ED模型。说话人数据显示出高变异性，两个后缀表明“规则”的行为，出现更频繁的语音非典型输入。编码器-解码器模型确实概括了最频繁产生的复数类，但没有显示出这些其他复数标记的类似人类的可变性或“规则”扩展。我们的结论是，现代神经模型可能仍在努力解决少数群体的泛化问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
