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20182021
most citedConversational AI: The Science Behind the Alexa Prize

200 citations · 264 across the 3 of their papers we have counts for

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Showing cs.CLShow all

6 papers · 1 filter

cs.CL20213 cited

Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems

Anish Acharya, Suranjit Adhikari, Sanchit Agarwal +28

Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Trai…

cs.CL2019

Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators

Sanghyun Yi, Rahul Goel, Chandra Khatri +6

Encoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a…

cs.CL2019

Natural Language Generation at Scale: A Case Study for Open Domain Question Answering

Alessandra Cervone, Chandra Khatri, Rahul Goel +4

Current approaches to Natural Language Generation (NLG) for dialog mainly focus on domain-specific, task-oriented applications (e.g. restaurant booking) using limited ontologies (u…

cs.CL201861 cited

Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize

Chandra Khatri, Behnam Hedayatnia, Anu Venkatesh +19

Building open domain conversational systems that allow users to have engaging conversations on topics of their choice is a challenging task. Alexa Prize was launched in 2016 to tac…

cs.CL2018

Detecting Offensive Content in Open-domain Conversations using Two Stage Semi-supervision

Chandra Khatri, Behnam Hedayatnia, Rahul Goel +3

As open-ended human-chatbot interaction becomes commonplace, sensitive content detection gains importance. In this work, we propose a two stage semi-supervised approach to bootstra…

cs.CL2018

Contextual Topic Modeling For Dialog Systems

Chandra Khatri, Rahul Goel, Behnam Hedayatnia +4

Accurate prediction of conversation topics can be a valuable signal for creating coherent and engaging dialog systems. In this work, we focus on context-aware topic classification…