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Gokhan Tur

Gokhan Tur
1 BLOG ARTICLES 4 RESEARCH PAPERS
Gokhan Tur is a Director of Engineering with Uber AI.

Engineering Blog Articles

First Uber Science Symposium: Discussing the Next Generation of RL, NLP, ConvAI, and DL

The Uber Science Symposium featured talks from members of the broader scientific community about the the latest innovations in RL, NLP, and other fields.

Research Papers

Flexibly-Structured Model for Task-Oriented Dialogues

L. Shu, P. Molino, M. Namazifar, H. Xu, B. Liu, H. Zheng, G. Tur
This paper proposes a novel end-to-end architecture for task-oriented dialogue systems. It is based on a simple and practical yet very effective sequence-to-sequence approach, where language understanding and state tracking tasks are modeled jointly with a structured copy-augmented sequential decoder and a multi-label decoder for each slot. The policy engine and language generation tasks are modeled jointly following that. [...] [PDF]
2019

Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning

A. Papangelis, Y.-C. Wang, P. Molino, G. Tur
We present the first complete attempt at concurrently training conversational agents that communicate only via self-generated language. Using DSTC2 as seed data, we trained natural language understanding (NLU) and generation (NLG) networks for each agent and let the agents interact online. [...] [PDF]
Special Interest Group on Discourse and Dialogue (SIGDIAL), 2019

Incorporating the Structure of the Belief State in End-to-End Task-Oriented Dialogue Systems

L. Shu, P. Molino, M. Namazifar, B. Liu, H. Xu, H. Zheng, and G. Tur
End-to-end trainable networks try to overcome error propagation, lack of generalization and overall brittleness of traditional modularized task-oriented dialogue system architectures. Most proposed models expand on the sequence-to-sequence architecture. Some of them don’t track belief state, which makes it difficult to interact with ever-changing knowledge bases, while the ones that explicitly track the belief state do it with classifiers. The use of classifiers suffers from the out-of-vocabulary words problem, making these models hard to use in real-world applications with ever-changing knowledge bases. We propose Structured Belief Copy Network (SBCN), a novel end-to-end trainable architecture that allows for interaction with external symbolic knowledge bases and solves the out-of-vocabulary problem at the same time. [...] [PDF]
Conversational Intelligence Challenge at Conference on Neural Information Processing Systems (ConvAI @ NeurIPS), 2018

Can You be More Polite and Positive? Infusing Social Language into Task-Oriented Conversational Agents

Y.-C. Wang, R. Wang, G. Tur, H. Williams
Goal-oriented conversational agents are becoming ubiquitous in daily life for tasks ranging from personal assistants to customer support systems. For these systems to engage users and achieve their goals in a more natural manner, they need to not just provide informative replies and guide users through the problems but also to socialize with users. To this end, we extend the line of style transfer research on developing generative deep learning models to control for a specific style such as sentiment and personality. [...] [PDF]
Conversational Intelligence Challenge at Conference on Neural Information Processing Systems (ConvAI @ NeurIPS), 2018

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