activity
20192022
most citedInvestigation of Error Simulation Techniques for Learning Dialog Policies for Conversational Error Recovery

12 citations · 16 across the 5 of their papers we have counts for

collaborators

7 papers

stat.ML2022

Robust Nonparametric Distribution Forecast with Backtest-based Bootstrap and Adaptive Residual Selection

Longshaokan Wang, Lingda Wang, Mina Georgieva +6

Distribution forecast can quantify forecast uncertainty and provide various forecast scenarios with their corresponding estimated probabilities. Accurate distribution forecast is c…

cs.CL2020

Are Neural Open-Domain Dialog Systems Robust to Speech Recognition Errors in the Dialog History? An Empirical Study

Karthik Gopalakrishnan, Behnam Hedayatnia, Longshaokan Wang +2

Large end-to-end neural open-domain chatbots are becoming increasingly popular. However, research on building such chatbots has typically assumed that the user input is written in…

cs.CL20204 cited

Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors

Longshaokan Wang, Maryam Fazel-Zarandi, Aditya Tiwari +2

Speech-based virtual assistants, such as Amazon Alexa, Google assistant, and Apple Siri, typically convert users' audio signals to text data through automatic speech recognition (A…

stat.ML2019

High dimensional precision medicine from patient-derived xenografts

Naim U. Rashid, Daniel J. Luckett, Jingxiang Chen +8

The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers potential to improve patient outcomes by leveraging th…

cs.CL201912 cited

Investigation of Error Simulation Techniques for Learning Dialog Policies for Conversational Error Recovery

Maryam Fazel-Zarandi, Longshaokan Wang, Aditya Tiwari +1

Training dialog policies for speech-based virtual assistants requires a plethora of conversational data. The data collection phase is often expensive and time consuming due to huma…

cs.LG2019

Domain-Independent turn-level Dialogue Quality Evaluation via User Satisfaction Estimation

Praveen Kumar Bodigutla, Longshaokan Wang, Kate Ridgeway +4

An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversa…