Delexicalized transfer parsing for low-resource languages using transformed and combined treebanks

• Autores: Ayan Das, Mohammad Affan Zafar, Sudeshna Sarkar
• 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. 182-190
• Idioma: inglés
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• Resumen

  • This paper describes the IIT Kharagpur dependency parsing system in CoNLL-2017 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We primarily focus on the low resource languages (surprise languages). We have developed a framework to combine multiple treebanks to train parsers for low resource languages by a delexicalization method. We have applied transformation on the source language treebanks based on syntactic features of the low-resource language to improve perfor- mance of the parser. In the official evaluation, our system achieves macro-averaged LAS scores of 67.61 and 37.16 on the entire blind test data and the surprise language test data respectively.