Search Results for author:
Found 10 papers, 7 papers with code
Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation
Current language generation models suffer from issues such as repetition, incoherence, and hallucinations.
Diverse Keyphrase Generation with Neural Unlikelihood Training
Further, to encourage better model planning during the decoding process, we incorporate K-step ahead token prediction objective that computes both MLE and UL losses on future tokens as well.
Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation
Learning disentangled representations of real-world data is a challenging open problem.
Generating lyrics with variational autoencoder and multi-modal artist embeddings
We present a system for generating song lyrics lines conditioned on the style of a specified artist.
Natural Language Generation with Neural Variational Models
We discover that the traditional attention mechanism used in sequence-to-sequence VED models serves as a bypassing connection, thereby deteriorating the model's latent space.
Disentangled Representation Learning for Non-Parallel Text Style Transfer
This paper tackles the problem of disentangling the latent variables of style and content in language models.
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation
The variational autoencoder (VAE) imposes a probabilistic distribution (typically Gaussian) on the latent space and penalizes the Kullback--Leibler (KL) divergence between the posterior and prior.
Music Genre Classification using Machine Learning Techniques
Categorizing music files according to their genre is a challenging task in the area of music information retrieval (MIR).
Sound Audio and Speech Processing
Variational Attention for Sequence-to-Sequence Models
The variational encoder-decoder (VED) encodes source information as a set of random variables using a neural network, which in turn is decoded into target data using another neural network.
UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features
This paper describes our system for subtask-A: SDQC for RumourEval, task-8 of SemEval 2017.
Ranked #2 on
Stance Detection
on RumourEval
