How "deep" is learning word inflection?

Machine learning offers two basic strategies for morphology induction: lexical segmentation and surface word relation. The first one assumes that words can be segmented into morphemes. Inducing a novel inflected form requires identification of morphemic constituents and a strategy for their recombination. The second approach dispenses with segmentation: lexical representations form part of a network of associatively related inflected forms. Production of a novel form consists in filling in one empty node in the network. Here, we present the results of a recurrent LSTM network that learns to fill in paradigm cells of incomplete verb paradigms. Although the process is not based on morpheme segmentation, the model shows sensitivity to stem selection and stem-ending boundaries.

La letteratura offre due strategie di base per l'induzione morfologica. La prima presuppone la segmentazione delle forme lessicali in morfemi e genera parole nuove ricombinando morfemi conosciuti; la seconda si basa sulle relazioni di una forma con le altre forme del suo paradigma, e genera una parola sconosciuta riempiendo una cella vuota del paradigma. In questo articolo, presentiamo i risultati di una rete LSTM ricorrente, capace di imparare a generare nuove forme verbali a partire da forme già note non segmentate. Ciononostante, la rete acquisisce una conoscenza implicita del tema verbale e del confine con la terminazione flessionale.