@inproceedings{vacareanu-etal-2020-parsing,
title = "Parsing as Tagging",
author = "Vacareanu, Robert and
Gouveia Barbosa, George Caique and
Valenzuela-Esc{\'a}rcega, Marco A. and
Surdeanu, Mihai",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.643",
pages = "5225--5231",
abstract = "We propose a simple yet accurate method for dependency parsing that treats parsing as tagging (PaT). That is, our approach addresses the parsing of dependency trees with a sequence model implemented with a bidirectional LSTM over BERT embeddings, where the {``}tag{''} to be predicted at each token position is the relative position of the corresponding head. For example, for the sentence John eats cake, the tag to be predicted for the token cake is -1 because its head (eats) occurs one token to the left. Despite its simplicity, our approach performs well. For example, our approach outperforms the state-of-the-art method of (Fern{\'a}ndez-Gonz{\'a}lez and G{\'o}mez-Rodr{\'\i}guez, 2019) on Universal Dependencies (UD) by 1.76{\%} unlabeled attachment score (UAS) for English, 1.98{\%} UAS for French, and 1.16{\%} UAS for German. On average, on 12 UD languages, our method with minimal tuning performs comparably with this state-of-the-art approach: better by 0.11{\%} UAS, and worse by 0.58{\%} LAS.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>We propose a simple yet accurate method for dependency parsing that treats parsing as tagging (PaT). That is, our approach addresses the parsing of dependency trees with a sequence model implemented with a bidirectional LSTM over BERT embeddings, where the “tag” to be predicted at each token position is the relative position of the corresponding head. For example, for the sentence John eats cake, the tag to be predicted for the token cake is -1 because its head (eats) occurs one token to the left. Despite its simplicity, our approach performs well. For example, our approach outperforms the state-of-the-art method of (Fernández-González and Gómez-Rodríguez, 2019) on Universal Dependencies (UD) by 1.76% unlabeled attachment score (UAS) for English, 1.98% UAS for French, and 1.16% UAS for German. On average, on 12 UD languages, our method with minimal tuning performs comparably with this state-of-the-art approach: better by 0.11% UAS, and worse by 0.58% LAS.</abstract>
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%0 Conference Proceedings
%T Parsing as Tagging
%A Vacareanu, Robert
%A Gouveia Barbosa, George Caique
%A Valenzuela-Escárcega, Marco A.
%A Surdeanu, Mihai
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F vacareanu-etal-2020-parsing
%X We propose a simple yet accurate method for dependency parsing that treats parsing as tagging (PaT). That is, our approach addresses the parsing of dependency trees with a sequence model implemented with a bidirectional LSTM over BERT embeddings, where the “tag” to be predicted at each token position is the relative position of the corresponding head. For example, for the sentence John eats cake, the tag to be predicted for the token cake is -1 because its head (eats) occurs one token to the left. Despite its simplicity, our approach performs well. For example, our approach outperforms the state-of-the-art method of (Fernández-González and Gómez-Rodríguez, 2019) on Universal Dependencies (UD) by 1.76% unlabeled attachment score (UAS) for English, 1.98% UAS for French, and 1.16% UAS for German. On average, on 12 UD languages, our method with minimal tuning performs comparably with this state-of-the-art approach: better by 0.11% UAS, and worse by 0.58% LAS.
%U https://aclanthology.org/2020.lrec-1.643
%P 5225-5231
Markdown (Informal)
[Parsing as Tagging](https://aclanthology.org/2020.lrec-1.643) (Vacareanu et al., LREC 2020)
ACL
- Robert Vacareanu, George Caique Gouveia Barbosa, Marco A. Valenzuela-Escárcega, and Mihai Surdeanu. 2020. Parsing as Tagging. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 5225–5231, Marseille, France. European Language Resources Association.