activity
20162023
most citedLearning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders

164 citations · 515 across the 28 of their papers we have counts for

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Showing 2019Show all

7 papers · 1 filter

cs.CL2019★ 2 cited

CMU GetGoing: An Understandable and Memorable Dialog System for Seniors

Shikib Mehri, Alan W Black, Maxine Eskenazi

Voice-based technologies are typically developed for the average user, and thus generally not tailored to the specific needs of any subgroup of the population, like seniors. This p…

cs.CL2019

Multi-Granularity Representations of Dialog

Shikib Mehri, Maxine Eskenazi

Neural models of dialog rely on generalized latent representations of language. This paper introduces a novel training procedure which explicitly learns multiple representations of…

cs.CL2019

Structured Fusion Networks for Dialog

Shikib Mehri, Tejas Srinivasan, Maxine Eskenazi

Neural dialog models have exhibited strong performance, however their end-to-end nature lacks a representation of the explicit structure of dialog. This results in a loss of genera…

cs.CL2019

Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References

Prakhar Gupta, Shikib Mehri, Tiancheng Zhao +3

The aim of this paper is to mitigate the shortcomings of automatic evaluation of open-domain dialog systems through multi-reference evaluation. Existing metrics have been shown to…

cs.CL2019

Pretraining Methods for Dialog Context Representation Learning

Shikib Mehri, Evgeniia Razumovskaia, Tiancheng Zhao +1

This paper examines various unsupervised pretraining objectives for learning dialog context representations. Two novel methods of pretraining dialog context encoders are proposed,…

cs.CL2019★ 22 cited

Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models

Tiancheng Zhao, Kaige Xie, Maxine Eskenazi

Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge. Common practice has been to use…

cs.CL2019★ 10 cited

Beyond Turing: Intelligent Agents Centered on the User

Maxine Eskenazi, Shikib Mehri, Evgeniia Razumovskaia +1

Most research on intelligent agents centers on the agent and not on the user. We look at the origins of agent-centric research for slot-filling, gaming and chatbot agents. We then…