Eslam Kamal


2020

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Conversation Learner - A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems
Swadheen Shukla | Lars Liden | Shahin Shayandeh | Eslam Kamal | Jinchao Li | Matt Mazzola | Thomas Park | Baolin Peng | Jianfeng Gao
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Traditionally, industry solutions for building a task-oriented dialog system have relied on helping dialog authors define rule-based dialog managers, represented as dialog flows. While dialog flows are intuitively interpretable and good for simple scenarios, they fall short of performance in terms of the flexibility needed to handle complex dialogs. On the other hand, purely machine-learned models can handle complex dialogs, but they are considered to be black boxes and require large amounts of training data. In this demonstration, we showcase Conversation Learner, a machine teaching tool for building dialog managers. It combines the best of both approaches by enabling dialog authors to create a dialog flow using familiar tools, converting the dialog flow into a parametric model (e.g., neural networks), and allowing dialog authors to improve the dialog manager (i.e., the parametric model) over time by leveraging user-system dialog logs as training data through a machine teaching interface.

2016

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RDI_Team at SemEval-2016 Task 3: RDI Unsupervised Framework for Text Ranking
Ahmed Magooda | Amr Gomaa | Ashraf Mahgoub | Hany Ahmed | Mohsen Rashwan | Hazem Raafat | Eslam Kamal | Ahmad Al Sallab
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Fast and easy language understanding for dialog systems with Microsoft Language Understanding Intelligent Service (LUIS)
Jason D. Williams | Eslam Kamal | Mokhtar Ashour | Hani Amr | Jessica Miller | Geoff Zweig
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue