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• W2891339190 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." @default.
• W2891339190 created "2018-09-27" @default.
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• W2891339190 date "2018-01-01" @default.
• W2891339190 modified "2023-10-16" @default.
• W2891339190 title "Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data" @default.
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• W2891339190 doi "https://doi.org/10.18653/v1/d18-1311" @default.
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