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20182020
most citedConversational AI: The Science Behind the Alexa Prize

200 citations · 326 across the 6 of their papers we have counts for

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11 papers · 1 filter

cs.CL20202 cited

Joint Contextual Modeling for ASR Correction and Language Understanding

Yue Weng, Sai Sumanth Miryala, Chandra Khatri +8

The quality of automatic speech recognition (ASR) is critical to Dialogue Systems as ASR errors propagate to and directly impact downstream tasks such as language understanding (LU…

cs.CL2020

Exploration Based Language Learning for Text-Based Games

Andrea Madotto, Mahdi Namazifar, Joost Huizinga +7

This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describ…

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…