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Mahdi Namazifar

Mahdi Namazifar
2 BLOG ARTICLES 2 RESEARCH PAPERS
Mahdi Namazifar is a tech lead for Uber's NLP & Conversational AI team.

Engineering Blog Articles

Introducing the Plato Research Dialogue System: A Flexible Conversational AI Platform

The Plato Research Dialogue System enables experts and non-experts alike to quickly build, train, and deploy conversational AI agents.

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

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

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