A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations

• Autores: KyungTae Lim, Thierry Poibeau
• Localización: Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies : August 3-4, 2017 Vancouver, Canada / coord. por Jan Hajic, 2017, ISBN 978-1-945626-70-8, págs. 63-70
• Idioma: inglés
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• Resumen

  • In this paper, we present our multilingual dependency parser developed for the CoNLL 2017 UD Shared Taskdealing with “Multilingual Parsing from Raw Text to Universal Dependencies”.

    Our parser extends the monolingual BIST-parser as a multi-source multilingual trainable parser. Thanks to multilingual word embeddings and one hot encodings f or languages, our system can use both monolingual and multi-source training. We trained 69 monolingual language models and 13 multilingual models for the shared task. Our multilingual approach making use of different resources yield better results than the monolingual approach for 11 languages. Our system ranked 5th and achieved 70.93 overall LAS score over the 81 test corpora (macro-averaged LAS F1 score).