pith. the verified trust layer for science. sign in

arxiv: 1709.06033 · v1 · pith:5W6J5XS3new · submitted 2017-09-18 · 💻 cs.CL

Sequence to Sequence Learning for Event Prediction

classification 💻 cs.CL
keywords approacheventbleulearningpredictionscoresequenceannotation
0
0 comments X p. Extension
Add this Pith Number to your LaTeX paper What is a Pith Number?
\usepackage{pith}
\pithnumber{5W6J5XS3}

Prints a linked pith:5W6J5XS3 badge after your title and writes the identifier into PDF metadata. Compiles on arXiv with no extra files. Learn more

read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.