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Sequence to Sequence Learning for Event Prediction

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arxiv 1709.06033 v1 pith:5W6J5XS3 submitted 2017-09-18 cs.CL

classification cs.CL
keywords approacheventbleulearningpredictionscoresequenceannotation
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents an approach to the task of predicting an event description from a preceding sentence in a text. Our approach explores sequence-to-sequence learning using a bidirectional multi-layer recurrent neural network. Our approach substantially outperforms previous work in terms of the BLEU score on two datasets derived from WikiHow and DeScript respectively. Since the BLEU score is not easy to interpret as a measure of event prediction, we complement our study with a second evaluation that exploits the rich linguistic annotation of gold paraphrase sets of events.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TransSent: Towards Generation of Structured Sentences with Discourse Marker

    cs.CL 2019-09 conditional novelty 6.0 of 10

    TransSent generates a tail discourse from a head discourse and a discourse marker by treating the marker as a translation in embedding space, with new datasets and improved scores over baselines.

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