no code implementations • 19 Aug 2019 • Praveen Kumar Bodigutla, Longshaokan Wang, Kate Ridgeway, Joshua Levy, Swanand Joshi, Alborz Geramifard, Spyros Matsoukas
An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management.
no code implementations • 18 Nov 2019 • Praveen Kumar Bodigutla, Lazaros Polymenakos, Spyros Matsoukas
To address these gaps, we created a new Response Quality annotation scheme, introduced five new domain-independent feature sets and experimented with six machine learning models to estimate User Satisfaction at both turn and dialogue level.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Praveen Kumar Bodigutla, Aditya Tiwari, Josep Valls Vargas, Lazaros Polymenakos, Spyros Matsoukas
Dialogue level quality estimation is vital for optimizing data driven dialogue management.
no code implementations • 10 Aug 2021 • Praveen Kumar Bodigutla
To recommend high quality related search queries, we train a Deep Reinforcement Learning model to predict the query a user would enter next.
no code implementations • 12 Feb 2023 • Xavier Amatriain, Ananth Sankar, Jie Bing, Praveen Kumar Bodigutla, Timothy J. Hazen, Michaeel Kazi
The goal of this paper is to offer a somewhat comprehensive but simple catalog and classification of the most popular Transformer models.