1 citations · 1 across the 4 of their papers we have counts for
7 papers
Using Pause Information for More Accurate Entity Recognition
Sahas Dendukuri, Pooja Chitkara, Joel Ruben Antony Moniz +3
Entity tags in human-machine dialog are integral to natural language understanding (NLU) tasks in conversational assistants. However, current systems struggle to accurately parse s…
Open-Domain Question Answering Goes Conversational via Question Rewriting
Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu +3
We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find a…
Generalized Reinforcement Meta Learning for Few-Shot Optimization
Raviteja Anantha, Stephen Pulman, Srinivas Chappidi
We present a generic and flexible Reinforcement Learning (RL) based meta-learning framework for the problem of few-shot learning. During training, it learns the best optimization a…
Lattice-based Improvements for Voice Triggering Using Graph Neural Networks
Pranay Dighe, Saurabh Adya, Nuoyu Li +6
Voice-triggered smart assistants often rely on detection of a trigger-phrase before they start listening for the user request. Mitigation of false triggers is an important aspect o…
Leveraging User Engagement Signals For Entity Labeling in a Virtual Assistant
Deepak Muralidharan, Justine Kao, Xiao Yang +8
Personal assistant AI systems such as Siri, Cortana, and Alexa have become widely used as a means to accomplish tasks through natural language commands. However, components in thes…
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant
Xi C. Chen, Adithya Sagar, Justine T. Kao +5
We describe a method for selecting relevant new training data for the LSTM-based domain selection component of our personal assistant system. Adding more annotated training data fo…