@inproceedings{zhao-etal-2021-structural,
title = "Structural Realization with {GGNN}s",
author = "Zhao, Jinman and
Penn, Gerald and
Ling, Huan",
editor = "Panchenko, Alexander and
Malliaros, Fragkiskos D. and
Logacheva, Varvara and
Jana, Abhik and
Ustalov, Dmitry and
Jansen, Peter",
booktitle = "Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.textgraphs-1.11",
doi = "10.18653/v1/2021.textgraphs-1.11",
pages = "115--124",
abstract = "In this paper, we define an abstract task called structural realization that generates words given a prefix of words and a partial representation of a parse tree. We also present a method for solving instances of this task using a Gated Graph Neural Network (GGNN). We evaluate it with standard accuracy measures, as well as with respect to perplexity, in which its comparison to previous work on language modelling serves to quantify the information added to a lexical selection task by the presence of syntactic knowledge. That the addition of parse-tree-internal nodes to this neural model should improve the model, with respect both to accuracy and to more conventional measures such as perplexity, may seem unsurprising, but previous attempts have not met with nearly as much success. We have also learned that transverse links through the parse tree compromise the model{'}s accuracy at generating adjectival and nominal parts of speech.",
}
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%0 Conference Proceedings
%T Structural Realization with GGNNs
%A Zhao, Jinman
%A Penn, Gerald
%A Ling, Huan
%Y Panchenko, Alexander
%Y Malliaros, Fragkiskos D.
%Y Logacheva, Varvara
%Y Jana, Abhik
%Y Ustalov, Dmitry
%Y Jansen, Peter
%S Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F zhao-etal-2021-structural
%X In this paper, we define an abstract task called structural realization that generates words given a prefix of words and a partial representation of a parse tree. We also present a method for solving instances of this task using a Gated Graph Neural Network (GGNN). We evaluate it with standard accuracy measures, as well as with respect to perplexity, in which its comparison to previous work on language modelling serves to quantify the information added to a lexical selection task by the presence of syntactic knowledge. That the addition of parse-tree-internal nodes to this neural model should improve the model, with respect both to accuracy and to more conventional measures such as perplexity, may seem unsurprising, but previous attempts have not met with nearly as much success. We have also learned that transverse links through the parse tree compromise the model’s accuracy at generating adjectival and nominal parts of speech.
%R 10.18653/v1/2021.textgraphs-1.11
%U https://aclanthology.org/2021.textgraphs-1.11
%U https://doi.org/10.18653/v1/2021.textgraphs-1.11
%P 115-124
Markdown (Informal)
[Structural Realization with GGNNs](https://aclanthology.org/2021.textgraphs-1.11) (Zhao et al., TextGraphs 2021)
ACL
- Jinman Zhao, Gerald Penn, and Huan Ling. 2021. Structural Realization with GGNNs. In Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15), pages 115–124, Mexico City, Mexico. Association for Computational Linguistics.