Search Results for author:
Found 10 papers, 1 papers with code
Extracting and Inferring Personal Attributes from Dialogue
Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes.
Bag of Experts Architectures for Model Reuse in Conversational Language Understanding
Slot tagging, the task of detecting entities in input user utterances, is a key component of natural language understanding systems for personal digital assistants.
Slot Tagging for Task Oriented Spoken Language Understanding in Human-to-Human Conversation Scenarios
In this work, we extend the task oriented LU problem to human-to-human (H2H) conversations, focusing on the slot tagging task.
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool
We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for the general task of labeling structured data with textual descriptions.
Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling
Neural natural language generation (NLG) and understanding (NLU) models are data-hungry and require massive amounts of annotated data to be competitive.
The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation
We propose a shared task on training instance selection for few-shot neural text generation.
Programmable Annotation with Diversed Heuristics and Data Denoising
To this end, we propose a novel data programming framework that can jointly construct labeled data for language generation and understanding tasks – by allowing the annotators to modify an automatically-inferred alignment rule set between sequence labels and text, instead of writing rules from scratch.
Improving Zero-Shot Multilingual Text Generation via Iterative Distillation
The demand for multilingual dialogue systems often requires a costly labeling process, where human translators derive utterances in low resource languages from resource rich language annotation.
Bridging the Skills Gap: Evaluating an AI-Assisted Provider Platform to Support Care Providers with Empathetic Delivery of Protocolized Therapy
We studied providers with and without expertise in mental health treatment delivering a therapy session using the platform with (intervention) and without (control) AI-assistance features.
