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- W2897739530 abstract "We explore whether it is possible to build lighter parsers, that are statistically equivalent to their corresponding standard version, for a wide set of languages showing different structures and morphologies. As testbed, we use the Universal Dependencies and transition-based dependency parsers trained on feed-forward networks. For these, most existing research assumes de facto standard embedded features and relies on pre-computation tricks to obtain speed-ups. We explore how these features and their size can be reduced and whether this translates into speed-ups with a negligible impact on accuracy. The experiments show that grand-daughter features can be removed for the majority of treebanks without a significant (negative or positive) LAS difference. They also show how the size of the embeddings can be notably reduced." @default.
- W2897739530 created "2018-10-26" @default.
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- W2897739530 date "2018-01-01" @default.
- W2897739530 modified "2023-10-03" @default.
- W2897739530 title "Transition-based Parsing with Lighter Feed-Forward Networks" @default.
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- W2897739530 doi "https://doi.org/10.18653/v1/w18-6019" @default.
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