activity
20182020
most citedAn Adversarial Learning Framework For A Persona-Based Multi-Turn Dialogue Model

2 citations · 3 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CL20201 cited

DLGNet-Task: An End-to-end Neural Network Framework for Modeling Multi-turn Multi-domain Task-Oriented Dialogue

Oluwatobi O. Olabiyi, Prarthana Bhattarai, C. Bayan Bruss +1

Task oriented dialogue (TOD) requires the complex interleaving of a number of individually controllable components with strong guarantees for explainability and verifiability. This…

cs.CL2019

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…

cs.CL2019

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…

cs.CL20192 cited

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…

cs.CL2019

A Persona-based Multi-turn Conversation Model in an Adversarial Learning Framework

Oluwatobi O. Olabiyi, Anish Khazane, Erik T. Mueller

In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to multi-turn dialogue by modifying the state-of-the-art hredGAN archite…

cs.CL2018

Multi-turn Dialogue Response Generation in an Adversarial Learning Framework

Oluwatobi Olabiyi, Alan Salimov, Anish Khazane +1

We propose an adversarial learning approach for generating multi-turn dialogue responses. Our proposed framework, hredGAN, is based on conditional generative adversarial networks (…