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Title: Cross-Lingual Dependency Parsing with Unlabeled Auxiliary Languages
Cross-lingual transfer learning has become an important weapon to battle the unavailability of annotated resources for low-resource languages. One of the fundamental techniques to transfer across languages is learning language-agnostic representations, in the form of word embeddings or contextual encodings. In this work, we propose to leverage unannotated sentences from auxiliary languages to help learning language-agnostic representations. Specifically, we explore adversarial training for learning contextual encoders that produce invariant representations across languages to facilitate cross-lingual transfer. We conduct experiments on cross-lingual dependency parsing where we train a dependency parser on a source language and transfer it to a wide range of target languages. Experiments on 28 target languages demonstrate that adversarial training significantly improves the overall transfer performances under several different settings. We conduct a careful analysis to evaluate the language-agnostic representations resulted from adversarial training.  more » « less
Award ID(s):
1760523
PAR ID:
10144865
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Page Range / eLocation ID:
372 - 382
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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