47 citations · 102 across the 8 of their papers we have counts for
9 papers
Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation
Kushal Arora, Layla El Asri, Hareesh Bahuleyan +1
Current language generation models suffer from issues such as repetition, incoherence, and hallucinations. An often-repeated hypothesis is that this brittleness of generation model…
Diverse Keyphrase Generation with Neural Unlikelihood Training
Hareesh Bahuleyan, Layla El Asri
In this paper, we study sequence-to-sequence (S2S) keyphrase generation models from the perspective of diversity. Recent advances in neural natural language generation have made po…
Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation
Vikash Balasubramanian, Ivan Kobyzev, Hareesh Bahuleyan +2
Learning disentangled representations of real-world data is a challenging open problem. Most previous methods have focused on either supervised approaches which use attribute label…
Generating lyrics with variational autoencoder and multi-modal artist embeddings
Olga Vechtomova, Hareesh Bahuleyan, Amirpasha Ghabussi +1
We present a system for generating song lyrics lines conditioned on the style of a specified artist. The system uses a variational autoencoder with artist embeddings. We propose th…
Disentangled Representation Learning for Non-Parallel Text Style Transfer
Vineet John, Lili Mou, Hareesh Bahuleyan +1
This paper tackles the problem of disentangling the latent variables of style and content in language models. We propose a simple yet effective approach, which incorporates auxilia…
Natural Language Generation with Neural Variational Models
Hareesh Bahuleyan
In this thesis, we explore the use of deep neural networks for generation of natural language. Specifically, we implement two sequence-to-sequence neural variational models - varia…
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation
Hareesh Bahuleyan, Lili Mou, Hao Zhou +1
The variational autoencoder (VAE) imposes a probabilistic distribution (typically Gaussian) on the latent space and penalizes the Kullback--Leibler (KL) divergence between the post…
Music Genre Classification using Machine Learning Techniques
Hareesh Bahuleyan
Categorizing music files according to their genre is a challenging task in the area of music information retrieval (MIR). In this study, we compare the performance of two classes o…
Variational Attention for Sequence-to-Sequence Models
Hareesh Bahuleyan, Lili Mou, Olga Vechtomova +1
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…