@inproceedings{stanovsky-dagan-2018-semantics,
title = "Semantics as a Foreign Language",
author = "Stanovsky, Gabriel and
Dagan, Ido",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1263",
doi = "10.18653/v1/D18-1263",
pages = "2412--2421",
abstract = "We propose a novel approach to semantic dependency parsing (SDP) by casting the task as an instance of multi-lingual machine translation, where each semantic representation is a different foreign dialect. To that end, we first generalize syntactic linearization techniques to account for the richer semantic dependency graph structure. Following, we design a neural sequence-to-sequence framework which can effectively recover our graph linearizations, performing almost on-par with previous SDP state-of-the-art while requiring less parallel training annotations. Beyond SDP, our linearization technique opens the door to integration of graph-based semantic representations as features in neural models for downstream applications.",
}
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%0 Conference Proceedings
%T Semantics as a Foreign Language
%A Stanovsky, Gabriel
%A Dagan, Ido
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F stanovsky-dagan-2018-semantics
%X We propose a novel approach to semantic dependency parsing (SDP) by casting the task as an instance of multi-lingual machine translation, where each semantic representation is a different foreign dialect. To that end, we first generalize syntactic linearization techniques to account for the richer semantic dependency graph structure. Following, we design a neural sequence-to-sequence framework which can effectively recover our graph linearizations, performing almost on-par with previous SDP state-of-the-art while requiring less parallel training annotations. Beyond SDP, our linearization technique opens the door to integration of graph-based semantic representations as features in neural models for downstream applications.
%R 10.18653/v1/D18-1263
%U https://aclanthology.org/D18-1263
%U https://doi.org/10.18653/v1/D18-1263
%P 2412-2421
Markdown (Informal)
[Semantics as a Foreign Language](https://aclanthology.org/D18-1263) (Stanovsky & Dagan, EMNLP 2018)
ACL
- Gabriel Stanovsky and Ido Dagan. 2018. Semantics as a Foreign Language. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2412–2421, Brussels, Belgium. Association for Computational Linguistics.