Joint learning of morphology and syntax with cross-level contextual information flow
Abstract
We propose an integrated deep learning model for morphological segmentation, morpheme tagging, part-of-speech (POS) tagging, and syntactic parsing onto dependencies, using cross-level contextual information flow for every word, from segments to dependencies, with an attention mechanism at horizontal flow. Our model extends the work of Nguyen and Verspoor (2018) on joint POS tagging and dependency parsing to also include morphological segmentation and morphological tagging. We report our results on several languages. Primary focus is agglutination in morphology, in particular Turkish morphology, for which we demonstrate improved performance compared to models trained for individual tasks. Being one of the earlier efforts in joint modeling of syntax and morphology along with dependencies, we discuss prospective guidelines for future comparison.Citation
Can, B., Aleçakır, H., Manandhar, S. and Bozşahin, C. (2022) Joint learning of morphology and syntax with cross-level contextual information flow. Natural Language Engineering, 2022, pp. 1-33.Publisher
Cambridge University PressJournal
Natural Language EngineeringType
Journal articleLanguage
enDescription
© 2022 The Authors. Published by Cambridge University Press. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: 10.1017/S1351324921000371ISSN
1351-3249Sponsors
This research has been supported by Scientific and Technological Research Council of Turkey (TUBITAK), project number EEEAG-115E464.ae974a485f413a2113503eed53cd6c53
10.1017/S1351324921000371
Scopus Count
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/