@inproceedings{lange-etal-2020-adversarial,
title = "Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text",
author = {Lange, Lukas and
Iurshina, Anastasiia and
Adel, Heike and
Str{\"o}tgen, Jannik},
editor = "Gella, Spandana and
Welbl, Johannes and
Rei, Marek and
Petroni, Fabio and
Lewis, Patrick and
Strubell, Emma and
Seo, Minjoon and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.repl4nlp-1.14",
doi = "10.18653/v1/2020.repl4nlp-1.14",
pages = "103--109",
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.",
}
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%0 Conference Proceedings
%T Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text
%A Lange, Lukas
%A Iurshina, Anastasiia
%A Adel, Heike
%A Strötgen, Jannik
%Y Gella, Spandana
%Y Welbl, Johannes
%Y Rei, Marek
%Y Petroni, Fabio
%Y Lewis, Patrick
%Y Strubell, Emma
%Y Seo, Minjoon
%Y Hajishirzi, Hannaneh
%S Proceedings of the 5th Workshop on Representation Learning for NLP
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F lange-etal-2020-adversarial
%X 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.
%R 10.18653/v1/2020.repl4nlp-1.14
%U https://aclanthology.org/2020.repl4nlp-1.14
%U https://doi.org/10.18653/v1/2020.repl4nlp-1.14
%P 103-109
Markdown (Informal)
[Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text](https://aclanthology.org/2020.repl4nlp-1.14) (Lange et al., RepL4NLP 2020)
ACL