Sequence to Sequence Learning for Event Prediction

09/18/2017
by   Dai Quoc Nguyen, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/21/2017

Bandit Structured Prediction for Neural Sequence-to-Sequence Learning

Bandit structured prediction describes a stochastic optimization framewo...
research
02/21/2017

Learning to generate one-sentence biographies from Wikidata

We investigate the generation of one-sentence Wikipedia biographies from...
research
12/10/2020

Automatic Standardization of Colloquial Persian

The Iranian Persian language has two varieties: standard and colloquial....
research
09/13/2019

Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting

We study sequence-to-sequence (seq2seq) pre-training with data augmentat...
research
10/30/2016

Represent, Aggregate, and Constrain: A Novel Architecture for Machine Reading from Noisy Sources

In order to extract event information from text, a machine reading model...
research
05/20/2021

MLBiNet: A Cross-Sentence Collective Event Detection Network

We consider the problem of collectively detecting multiple events, parti...
research
04/24/2018

Improving Native Ads CTR Prediction by Large Scale Event Embedding and Recurrent Networks

Click through rate (CTR) prediction is very important for Native adverti...

Please sign up or login with your details

Forgot password? Click here to reset