Search Results for author:
Found 25 papers, 11 papers with code
Future Sight: Dynamic Story Generation with Large Pretrained Language Models
In this work, we propose Future Sight, a method for finetuning a pretrained generative transformer on the task of future conditioning.
LyricJam Sonic: A Generative System for Real-Time Composition and Musical Improvisation
Subsequently, it is difficult for artists to rediscover audio segments that might be suitable for use in their compositions from thousands of hours of recordings.
LyricJam: A system for generating lyrics for live instrumental music
We describe a real-time system that receives a live audio stream from a jam session and generates lyric lines that are congruent with the live music being played.
Towards A Multi-agent System for Online Hate Speech Detection
This paper envisions a multi-agent system for detecting the presence of hate speech in online social media platforms such as Twitter and Facebook.
Deep Learning for Text Style Transfer: A Survey
Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others.
Generation of lyrics lines conditioned on music audio clips
We present a system for generating novel lyrics lines conditioned on music audio.
Stylized Text Generation: Approaches and Applications
We start from the definition of style and different settings of stylized text generation, illustrated with various applications.
Iterative Edit-Based Unsupervised Sentence Simplification
We present a novel iterative, edit-based approach to unsupervised sentence simplification.
Ranked #5 on
Text Simplification
on Newsela
Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction
Automatic sentence summarization produces a shorter version of a sentence, while preserving its most important information.
Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation
Learning disentangled representations of real-world data is a challenging open problem.
Stylized Text Generation Using Wasserstein Autoencoders with a Mixture of Gaussian Prior
Moreover, we can train our model on relatively small datasets and learn the latent representation of a specified class by adding external data with other styles/classes to our dataset.
Adversarial Learning on the Latent Space for Diverse Dialog Generation
Generating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation but also being able to generate fluent sentences during inference.
Adaptive Fusion Techniques for Multimodal Data
Effective fusion of data from multiple modalities, such as video, speech, and text, is challenging due to the heterogeneous nature of multimodal data.
Generating Sentences from Disentangled Syntactic and Semantic Spaces
In this paper, we propose to generate sentences from disentangled syntactic and semantic spaces.
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
In the natural language processing literature, neural networks are becoming increasingly deeper and complex.
Ranked #55 on
Sentiment Analysis
on SST-2 Binary classification
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.
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.
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.
UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings
This paper describes the UWaterloo affect prediction system developed for EmoInt-2017.
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
UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation
The system uses text vectorization models, such as N-gram, TF-IDF and paragraph embeddings, coupled with regression model variants to predict the sentiment scores.
UWaterloo at SemEval-2017 Task 7: Locating the Pun Using Syntactic Characteristics and Corpus-based Metrics
The method achieved the best performance in the Heterographic category and the second best in the Homographic.
Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation
The system uses text vectorization models, such as N-gram, TF-IDF and paragraph embeddings, coupled with regression model variants to predict the sentiment scores.
