Search Results for author: Olga Vechtomova

Found 23 papers, 11 papers with code

LyricJam: A system for generating lyrics for live instrumental music

no code implementations3 Jun 2021 Olga Vechtomova, Gaurav Sahu, Dhruv Kumar

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

no code implementations3 May 2021 Gaurav Sahu, Robin Cohen, Olga Vechtomova

This paper envisions a multi-agent system for detecting the presence of hate speech in online social media platforms such as Twitter and Facebook.

Hate Speech Detection

Deep Learning for Text Style Transfer: A Survey

2 code implementations1 Nov 2020 Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, Rada Mihalcea

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.

Style Transfer Text Attribute Transfer +1

Stylized Text Generation: Approaches and Applications

no code implementations ACL 2020 Lili Mou, Olga Vechtomova

We start from the definition of style and different settings of stylized text generation, illustrated with various applications.

Style Transfer Text Generation

Adversarial Learning on the Latent Space for Diverse Dialog Generation

1 code implementation COLING 2020 Kashif Khan, Gaurav Sahu, Vikash Balasubramanian, Lili Mou, Olga Vechtomova

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

no code implementations EACL 2021 Gaurav Sahu, Olga Vechtomova

Effective fusion of data from multiple modalities, such as video, speech, and text, is challenging due to the heterogeneous nature of multimodal data.

Emotion Recognition Multimodal Machine Translation +1

Stylized Text Generation Using Wasserstein Autoencoders with a Mixture of Gaussian Prior

no code implementations10 Nov 2019 Amirpasha Ghabussi, Lili Mou, Olga Vechtomova

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.

Text Generation

Generating lyrics with variational autoencoder and multi-modal artist embeddings

no code implementations20 Dec 2018 Olga Vechtomova, Hareesh Bahuleyan, Amirpasha Ghabussi, Vineet John

We present a system for generating song lyrics lines conditioned on the style of a specified artist.

Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation

1 code implementation NAACL 2019 Hareesh Bahuleyan, Lili Mou, Hao Zhou, Olga Vechtomova

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.

Text Generation

Variational Attention for Sequence-to-Sequence Models

2 code implementations COLING 2018 Hareesh Bahuleyan, Lili Mou, Olga Vechtomova, Pascal Poupart

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.

UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation

1 code implementation SEMEVAL 2017 Vineet John, Olga Vechtomova

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.

Data Augmentation Sentiment Analysis

UWaterloo at SemEval-2017 Task 7: Locating the Pun Using Syntactic Characteristics and Corpus-based Metrics

no code implementations SEMEVAL 2017 Olga Vechtomova

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

1 code implementation29 Jul 2017 Vineet John, Olga Vechtomova

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.

Data Augmentation Sentiment Analysis

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