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Neural Factor Graph Models for Cross-lingual Morphological Tagging

Morphological analysis involves predicting the syntactic traits of a word (e.g. {POS: Noun, Case: Acc, Gender: Fem}). Previous work in morphological tagging improves performance for low-resource languages (LRLs) through cross-lingual training with a high-resource language (HRL) from the same family, but is limited by the strict---often false---assumption that tag sets exactly overlap between the HRL and LRL. In this paper we propose a method for cross-lingual morphological tagging that aims to improve information sharing between languages by relaxing this assumption. The proposed model uses factorial conditional random fields with neural network potentials, making it possible to (1) utilize the expressive power of neural network representations to smooth over superficial differences in the surface forms, (2) model pairwise and transitive relationships between tags, and (3) accurately generate tag sets that are unseen or rare in the training data. Experiments on four languages from the Universal Dependencies Treebank demonstrate superior tagging accuracies over existing cross-lingual approaches.


CoNLL-U Parser ( : pip install conllu

PyTorch, version 0.3.0


To run the baseline tagger for a language pair Danish/Swedish,

python --gpu --langs da/sv --tgt_size 1000

To run the Neural Factor Graph Model,

python --gpu --langs da/sv --tgt_size 1000

The transitions and pairwise factors can be turned off with the --no_transitions and --no_pairwise arguments.

You can run evaluation with the argument --test and visualize the learnt parameter matrices with the --visualize argument.

Please set the --treebank_path argument to the appropriate path to UD treebanks.


The Universal Dependency Treebanks can be obtained from


This project is licensed under the MIT License - see the file for details


    title = {Neural Factor Graph Models for Cross-lingual Morphological Tagging},
    author = {Chaitanya Malaviya and Matthew R. Gormley and Graham Neubig},
    booktitle = {The 56th Annual Meeting of the Association for Computational Linguistics (ACL)},
    address = {Melbourne, Australia},
    month = {July},
    year = {2018}


This repo contains the code for the paper Neural Factor Graph Models for Cross-lingual Morphological Tagging.





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