Can Buglalilar, BurcuAlecakir, HuseyinManandhar, SureshBozşahin, Cem2021-11-112021-11-112022-01-20Can, B., Aleçakır, H., Manandhar, S. and Bozşahin, C. (2022) Joint learning of morphology and syntax with cross-level contextual information flow. Natural Language Engineering, Can, B., Aleçakır, H., Manandhar, S., & Bozşahin, C. (2022). Joint learning of morphology and syntax with cross-level contextual information flow. Natural Language Engineering, 28(6), pp. 763-795. doi:10.1017/S13513249210003711351-324910.1017/S1351324921000371http://hdl.handle.net/2436/624440© 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/S1351324921000371We 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.application/pdfendependency parsingattentionrecurrent neural networksmorphological taggingmorphological segmentationmorphologysyntaxdeep learningJournal articleNatural Language Engineering2021-11-08