1 research outputs found
Linguistically inspired morphological inflection with a sequence to sequence model
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%)Comment: 13 pages, 6 figure