@inproceedings{pitler-2021-incorporating,
title = "Incorporating Compositionality and Morphology into End-to-End Models",
author = "Pitler, Emily",
editor = "Oepen, Stephan and
Sagae, Kenji and
Tsarfaty, Reut and
Bouma, Gosse and
Seddah, Djam{\'e} and
Zeman, Daniel",
booktitle = "Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.iwpt-1.14",
doi = "10.18653/v1/2021.iwpt-1.14",
pages = "145",
abstract = "Many neural end-to-end systems today do not rely on syntactic parse trees, as much of the information that parse trees provide is encoded in the parameters of pretrained models. Lessons learned from parsing technologies and from taking a multilingual perspective, however, are still relevant even for end-to-end models. This talk will describe work that relies on compositionality in semantic parsing and in reading comprehension requiring numerical reasoning. We{'}ll then describe a new dataset that requires advances in multilingual modeling, and some approaches designed to better model morphology than off-the-shelf subword models that make some progress on these challenges.",
}
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<abstract>Many neural end-to-end systems today do not rely on syntactic parse trees, as much of the information that parse trees provide is encoded in the parameters of pretrained models. Lessons learned from parsing technologies and from taking a multilingual perspective, however, are still relevant even for end-to-end models. This talk will describe work that relies on compositionality in semantic parsing and in reading comprehension requiring numerical reasoning. We’ll then describe a new dataset that requires advances in multilingual modeling, and some approaches designed to better model morphology than off-the-shelf subword models that make some progress on these challenges.</abstract>
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%0 Conference Proceedings
%T Incorporating Compositionality and Morphology into End-to-End Models
%A Pitler, Emily
%Y Oepen, Stephan
%Y Sagae, Kenji
%Y Tsarfaty, Reut
%Y Bouma, Gosse
%Y Seddah, Djamé
%Y Zeman, Daniel
%S Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F pitler-2021-incorporating
%X Many neural end-to-end systems today do not rely on syntactic parse trees, as much of the information that parse trees provide is encoded in the parameters of pretrained models. Lessons learned from parsing technologies and from taking a multilingual perspective, however, are still relevant even for end-to-end models. This talk will describe work that relies on compositionality in semantic parsing and in reading comprehension requiring numerical reasoning. We’ll then describe a new dataset that requires advances in multilingual modeling, and some approaches designed to better model morphology than off-the-shelf subword models that make some progress on these challenges.
%R 10.18653/v1/2021.iwpt-1.14
%U https://aclanthology.org/2021.iwpt-1.14
%U https://doi.org/10.18653/v1/2021.iwpt-1.14
%P 145
Markdown (Informal)
[Incorporating Compositionality and Morphology into End-to-End Models](https://aclanthology.org/2021.iwpt-1.14) (Pitler, IWPT 2021)
ACL