Papers with generator network

3 papers
Open Event Extraction from Online Text using a Generative Adversarial Network (D19-1)

Copied to clipboard

Challenge: Existing approaches to extract structured representations of open-domain events are limited . a recent study shows that the model outperforms the baseline approaches for extracting events from online texts .
Approach: They propose an event extraction model based on Generative Adversarial Nets which captures latent events with a generator network and a discriminator to distinguish documents reconstructed from latent and original events.
Outcome: The proposed model outperforms baseline models on two Twitter and a news article datasets.
Generative Bridging Network for Neural Sequence Prediction (N18-1)

Copied to clipboard

Challenge: Existing approaches to improve the likelihood of sequence prediction models are based on MLE and teacher forcing.
Approach: They propose a Generative Bridging Network (GBN) that extends the point-wise ground truth to a bridge distribution conditioned on it and optimizes their KL-divergence.
Outcome: The proposed bridge module can improve on two recognized sequence prediction tasks and minimize learning burden.
Neural Topic Modeling with Cycle-Consistent Adversarial Training (2020.emnlp-main)

Copied to clipboard

Challenge: Recent advances on deep generative models have attracted significant interest in neural topic modeling.
Approach: They propose an adversarial-neural topic model which uses Dirichlet prior to capture the semantic patterns in latent topics.
Outcome: The proposed models outperform competing models on unsupervised/supervised topic modeling and text classification.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations