@inproceedings{rudinger-etal-2018-neural,
title = "Neural-{D}avidsonian Semantic Proto-role Labeling",
author = "Rudinger, Rachel and
Teichert, Adam and
Culkin, Ryan and
Zhang, Sheng and
Van Durme, Benjamin",
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/D18-1114",
doi = "10.18653/v1/D18-1114",
pages = "944--955",
abstract = "We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call NeuralDavidsonian: predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence. We demonstrate: (1) state-of-the-art results in SPRL, and (2) that our network naturally shares parameters between attributes, allowing for learning new attribute types with limited added supervision.",
}
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<abstract>We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call NeuralDavidsonian: predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence. We demonstrate: (1) state-of-the-art results in SPRL, and (2) that our network naturally shares parameters between attributes, allowing for learning new attribute types with limited added supervision.</abstract>
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%0 Conference Proceedings
%T Neural-Davidsonian Semantic Proto-role Labeling
%A Rudinger, Rachel
%A Teichert, Adam
%A Culkin, Ryan
%A Zhang, Sheng
%A Van Durme, Benjamin
%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 rudinger-etal-2018-neural
%X We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call NeuralDavidsonian: predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence. We demonstrate: (1) state-of-the-art results in SPRL, and (2) that our network naturally shares parameters between attributes, allowing for learning new attribute types with limited added supervision.
%R 10.18653/v1/D18-1114
%U https://aclanthology.org/D18-1114
%U https://doi.org/10.18653/v1/D18-1114
%P 944-955
Markdown (Informal)
[Neural-Davidsonian Semantic Proto-role Labeling](https://aclanthology.org/D18-1114) (Rudinger et al., EMNLP 2018)
ACL
- Rachel Rudinger, Adam Teichert, Ryan Culkin, Sheng Zhang, and Benjamin Van Durme. 2018. Neural-Davidsonian Semantic Proto-role Labeling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 944–955, Brussels, Belgium. Association for Computational Linguistics.