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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 2016Show all

5 papers · 1 filter

cs.CL2016

Graph-Community Detection for Cross-Document Topic Segment Relationship Identification

Pedro Mota, Maxine Eskenazi, Luisa Coheur

In this paper we propose a graph-community detection approach to identify cross-document relationships at the topic segment level. Given a set of related documents, we automaticall…

cs.AI2016★ 1 cited

DialPort: Connecting the Spoken Dialog Research Community to Real User Data

Tiancheng Zhao, Kyusong Lee, Maxine Eskenazi

This paper describes a new spoken dialog portal that connects systems produced by the spoken dialog academic research community and gives them access to real users. We introduce a…

cs.CL2016

Predicting the Relative Difficulty of Single Sentences With and Without Surrounding Context

Elliot Schumacher, Maxine Eskenazi, Gwen Frishkoff +1

The problem of accurately predicting relative reading difficulty across a set of sentences arises in a number of important natural language applications, such as finding and curati…

cs.AI2016★ 127 cited

Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning

Tiancheng Zhao, Maxine Eskenazi

This paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent Q-Networks (DRQN). The model is able to interface with a relational d…

cs.CL2016★ 9 cited

A Readability Analysis of Campaign Speeches from the 2016 US Presidential Campaign

Elliot Schumacher, Maxine Eskenazi

Readability is defined as the reading level of the speech from grade 1 to grade 12. It results from the use of the REAP readability analysis (vocabulary - Collins-Thompson and Call…