Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text

Lukas Lange, Anastasiia Iurshina, Heike Adel, Jannik Strötgen


Abstract
Although temporal tagging is still dominated by rule-based systems, there have been recent attempts at neural temporal taggers. However, all of them focus on monolingual settings. In this paper, we explore multilingual methods for the extraction of temporal expressions from text and investigate adversarial training for aligning embedding spaces to one common space. With this, we create a single multilingual model that can also be transferred to unseen languages and set the new state of the art in those cross-lingual transfer experiments.
Anthology ID:
2020.repl4nlp-1.14
Volume:
Proceedings of the 5th Workshop on Representation Learning for NLP
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | RepL4NLP | WS
SIG:
SIGREP
Publisher:
Association for Computational Linguistics
Note:
Pages:
103–109
Language:
URL:
https://www.aclweb.org/anthology/2020.repl4nlp-1.14
DOI:
10.18653/v1/2020.repl4nlp-1.14
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.repl4nlp-1.14.pdf
Video:
 http://slideslive.com/38929780