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Character-based Recurrent Neural Networks for Morphological Relational Reasoning

Journal article
Authors Olof Mogren
Richard Johansson
Published in Journal of Language Modeling
Volume 7
Issue 1
Pages 93-124
ISSN 2299-856X
Publication year 2019
Published at Department of Computer Science and Engineering (GU)
Pages 93-124
Language en
Links jlm.ipipan.waw.pl/index.php/JLM/art...
Keywords Morphology, neural networks, analogy
Subject categories Language Technology (Computational Linguistics)

Abstract

We present a model for predicting inflected word forms based on morphological analogies. Previous work includes rule-based algorithms that determine and copy affixes from one word to another, with limited support for varying inflectional patterns. In related tasks such as morphological reinflection, the algorithm is provided with an explicit enumeration of morphological features which may not be available in all cases. In contrast, our model is feature-free: instead of explicitly representing morphological features, the model is given a demo pair that implicitly specifies a morphological relation (such as write:writes specifying infinitive:present). Given this demo relation and a query word (e.g. watch), the model predicts the target word (e.g. watches). To address this task, we devise a character-based recurrent neural network architecture using three separate encoders and one decoder. Our experimental evaluation on five different languages shows tha the exact form can be predicted with high accuracy, consistently beating the baseline methods. Particularly, for English the prediction accuracy is 95.60%. The solution is not limited to copying affixes from the demo relation, but generalizes to words with varying inflectional patterns, and can abstract away from the orthographic level to the level of morphological forms.

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