@inproceedings{fisch-etal-2019-working,
title = "Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers",
author = "Fisch, Adam and
Guo, Jiang and
Barzilay, Regina",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1574",
doi = "10.18653/v1/D19-1574",
pages = "5714--5720",
abstract = "This paper explores the task of leveraging typology in the context of cross-lingual dependency parsing. While this linguistic information has shown great promise in pre-neural parsing, results for neural architectures have been mixed. The aim of our investigation is to better understand this state-of-the-art. Our main findings are as follows: 1) The benefit of typological information is derived from coarsely grouping languages into syntactically-homogeneous clusters rather than from learning to leverage variations along individual typological dimensions in a compositional manner; 2) Typology consistent with the actual corpus statistics yields better transfer performance; 3) Typological similarity is only a rough proxy of cross-lingual transferability with respect to parsing.",
}
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%0 Conference Proceedings
%T Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers
%A Fisch, Adam
%A Guo, Jiang
%A Barzilay, Regina
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F fisch-etal-2019-working
%X This paper explores the task of leveraging typology in the context of cross-lingual dependency parsing. While this linguistic information has shown great promise in pre-neural parsing, results for neural architectures have been mixed. The aim of our investigation is to better understand this state-of-the-art. Our main findings are as follows: 1) The benefit of typological information is derived from coarsely grouping languages into syntactically-homogeneous clusters rather than from learning to leverage variations along individual typological dimensions in a compositional manner; 2) Typology consistent with the actual corpus statistics yields better transfer performance; 3) Typological similarity is only a rough proxy of cross-lingual transferability with respect to parsing.
%R 10.18653/v1/D19-1574
%U https://aclanthology.org/D19-1574
%U https://doi.org/10.18653/v1/D19-1574
%P 5714-5720
Markdown (Informal)
[Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers](https://aclanthology.org/D19-1574) (Fisch et al., EMNLP-IJCNLP 2019)
ACL