论文标题
语言启发的形态学变化，序列与序列模型的序列
Linguistically inspired morphological inflection with a sequence to sequence model
论文作者
论文摘要
拐点是每种人类语言形态的重要组成部分，但近年来几乎没有努力统一语言理论和计算方法。弦操作的方法用于推断拐点变化；我们的研究问题是，神经网络是否能够学习以与人类在语言获取的早期阶段相似的方式进行拐点产生的拐点。我们正在使用拐点语料库（Metheniti和Neumann，2020）和单层SEQ2SEQ模型来检验该假设，在该假设中，学习并预测了曲折词缀为块，并且该单词stem被建模为字符序列，以说明依从性。我们的基于字符的模型通过预测词干到字符和拐点词形作为字符块，从而创造了变化。我们进行了三个实验，以创建鉴于引理以及一组输入和目标特征的单词的弯曲形式，将我们的体系结构与基于主流角色的模型与相同的超参数，训练和测试集进行了比较。总体而言，对于17种语言，我们注意到对已知的引理（+0.68％）的变化很小，但在预测未知词的变形形式（+3.7％）（+3.7％）和预测低资源场景（+1.09％）时，我们的模型表现稳步更好。
Inflection is an essential part of every human language's morphology, yet little effort has been made to unify linguistic theory and computational methods in recent years. Methods of string manipulation are used to infer inflectional changes; our research question is whether a neural network would be capable of learning inflectional morphemes for inflection production in a similar way to a human in early stages of language acquisition. We are using an inflectional corpus (Metheniti and Neumann, 2020) and a single layer seq2seq model to test this hypothesis, in which the inflectional affixes are learned and predicted as a block and the word stem is modelled as a character sequence to account for infixation. Our character-morpheme-based model creates inflection by predicting the stem character-to-character and the inflectional affixes as character blocks. We conducted three experiments on creating an inflected form of a word given the lemma and a set of input and target features, comparing our architecture to a mainstream character-based model with the same hyperparameters, training and test sets. Overall for 17 languages, we noticed small improvements on inflecting known lemmas (+0.68%) but steadily better performance of our model in predicting inflected forms of unknown words (+3.7%) and small improvements on predicting in a low-resource scenario (+1.09%)
