Search Results for author:
Found 6 papers, 1 papers with code
Joint Contextual Modeling for ASR Correction and Language Understanding
As a baseline approach, we trained task-specific Statistical Language Models (SLM) and fine-tuned state-of-the-art Generalized Pre-training (GPT) Language Model to re-rank the n-best ASR hypotheses, followed by a model to identify the dialog act and slots.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Exploration Based Language Learning for Text-Based Games
In this work, we propose to use the exploration approach of Go-Explore for solving text-based games.
Flexibly-Structured Model for Task-Oriented Dialogues
It is based on a simple and practical yet very effective sequence-to-sequence approach, where language understanding and state tracking tasks are modeled jointly with a structured copy-augmented sequential decoder and a multi-label decoder for each slot.
OCC: A Smart Reply System for Efficient In-App Communications
Our system consists of two major components: intent detection and reply retrieval, which are very different from standard smart reply systems where the task is to directly predict a reply.
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks
For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues.
Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI
A novel deep learning architecture (XmasNet) based on convolutional neural networks was developed for the classification of prostate cancer lesions, using the 3D multiparametric MRI data provided by the PROSTATEx challenge.
