Research Portal (King's College London)
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Abstract
We show how eye-tracking corpora can be used to improve sentence compression models, presenting a novel multi-task learning algorithm based on multi-layer LSTMs.We obtain performance competitive with or better than state-of-the-art approaches.
Keywords
Computer Science
Neural ComputationLong Short-Term Memory
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A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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Sentence Compression by Deletion with LSTMs
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Information Processing & ManagementDiscriminative sentence compression with conditional random fields
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This paper devise several features for Conditional Random Fields that allow it to incorporate information on nonlinear relations among words and address the issue of data paucity by collecting data from RSS feeds available on the Internet, and turning them into training data for use with CRF.
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30 Citations2000Yvonne Canning, John Tait +2 more
SYSTAR (SYntactic Simplification of Text for Aphasic Readers), the PSet module which splits compound sentences, activises seven agentive passive clause types, and resolves and replaces eight frequently occurring anaphoric pronouns is described.
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Down-stream effects of tree-to-dependency conversions
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This paper evaluates the down-stream effect of choice of conversion scheme, showing that it has dramatic impact on end results.
Reading metrics for estimating task efficiency with MT output
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It is shown that metrics derived from recording gaze while reading, are better proxies for machine translation quality than automated metrics.
