@inproceedings{wang-etal-2018-improved,
title = "Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data",
author = "Wang, Wenhui and
Chang, Baobao and
Mansur, Mairgup",
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/D18-1311",
doi = "10.18653/v1/D18-1311",
pages = "2857--2863",
abstract = "Pre-trained word embeddings and language model have been shown useful in a lot of tasks. However, both of them cannot directly capture word connections in a sentence, which is important for dependency parsing given its goal is to establish dependency relations between words. In this paper, we propose to implicitly capture word connections from unlabeled data by a word ordering model with self-attention mechanism. Experiments show that these implicit word connections do improve our parsing model. Furthermore, by combining with a pre-trained language model, our model gets state-of-the-art performance on the English PTB dataset, achieving 96.35{\%} UAS and 95.25{\%} LAS.",
}
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<abstract>Pre-trained word embeddings and language model have been shown useful in a lot of tasks. However, both of them cannot directly capture word connections in a sentence, which is important for dependency parsing given its goal is to establish dependency relations between words. In this paper, we propose to implicitly capture word connections from unlabeled data by a word ordering model with self-attention mechanism. Experiments show that these implicit word connections do improve our parsing model. Furthermore, by combining with a pre-trained language model, our model gets state-of-the-art performance on the English PTB dataset, achieving 96.35% UAS and 95.25% LAS.</abstract>
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%0 Conference Proceedings
%T Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data
%A Wang, Wenhui
%A Chang, Baobao
%A Mansur, Mairgup
%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 wang-etal-2018-improved
%X Pre-trained word embeddings and language model have been shown useful in a lot of tasks. However, both of them cannot directly capture word connections in a sentence, which is important for dependency parsing given its goal is to establish dependency relations between words. In this paper, we propose to implicitly capture word connections from unlabeled data by a word ordering model with self-attention mechanism. Experiments show that these implicit word connections do improve our parsing model. Furthermore, by combining with a pre-trained language model, our model gets state-of-the-art performance on the English PTB dataset, achieving 96.35% UAS and 95.25% LAS.
%R 10.18653/v1/D18-1311
%U https://aclanthology.org/D18-1311
%U https://doi.org/10.18653/v1/D18-1311
%P 2857-2863
Markdown (Informal)
[Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data](https://aclanthology.org/D18-1311) (Wang et al., EMNLP 2018)
ACL