2 citations · 5 across the 4 of their papers we have counts for
8 papers
Learning to Control Latent Representations for Few-Shot Learning of Named Entities
Omar U. Florez, Erik Mueller
Humans excel in continuously learning with small data without forgetting how to solve old problems. However, neural networks require large datasets to compute latent representation…
Aging Memories Generate More Fluent Dialogue Responses with Memory Augmented Neural Networks
Omar U. Florez, Erik Mueller
Memory Networks have emerged as effective models to incorporate Knowledge Bases (KB) into neural networks. By storing KB embeddings into a memory component, these models can learn…
Adversarial Bootstrapping for Dialogue Model Training
Oluwatobi Olabiyi, Erik T. Mueller, Christopher Larson +1
Open domain neural dialogue models, despite their successes, are known to produce responses that lack relevance, diversity, and in many cases coherence. These shortcomings stem fro…
DLGNet: A Transformer-based Model for Dialogue Response Generation
Oluwatobi Olabiyi, Erik T. Mueller
Neural dialogue models, despite their successes, still suffer from lack of relevance, diversity, and in many cases coherence in their generated responses. These issues can attribut…
An Adversarial Learning Framework For A Persona-Based Multi-Turn Dialogue Model
Oluwatobi Olabiyi, Anish Khazane, Alan Salimov +1
In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hred…
Telephonetic: Making Neural Language Models Robust to ASR and Semantic Noise
Chris Larson, Tarek Lahlou, Diana Mingels +2
Speech processing systems rely on robust feature extraction to handle phonetic and semantic variations found in natural language. While techniques exist for desensitizing features…