Search Results for author:
Found 12 papers, 8 papers with code
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis
Our evaluation results on the single-task polarity prediction show that our approach outperforms the previous state-of-the-art (based on BERT) on average performance by a large margins in few-shot and full-shot settings.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models
In this work, we explore joint energy-based model (EBM) training during the finetuning of pretrained text encoders (e. g., Roberta) for natural language understanding (NLU) tasks.
A Simple Language Model for Task-Oriented Dialogue
Task-oriented dialogue is often decomposed into three tasks: understanding user input, deciding actions, and generating a response.
Ranked #2 on
Response Generation
on MMConv
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
Over-dependence on domain ontology and lack of knowledge sharing across domains are two practical and yet less studied problems of dialogue state tracking.
Ranked #15 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+2
Toward Scalable Neural Dialogue State Tracking Model
The latency in the current neural based dialogue state tracking models prohibits them from being used efficiently for deployment in production systems, albeit their highly accurate performance.
Ranked #7 on
Dialogue State Tracking
on Wizard-of-Oz
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation
In low-resource supervised setting, the results show that our approach improves absolute performance by 14% and 4% when adapting SVHN to MNIST and vice versa, respectively, which outperforms unsupervised domain adaptation methods that require high-resource unlabeled target domain.
A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Speech Domain Adaptation
Domain adaptation plays an important role for speech recognition models, in particular, for domains that have low resources.
Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network
The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans.
Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network
The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans.
Structured Sparse Convolutional Autoencoder
This paper aims to improve the feature learning in Convolutional Networks (Convnet) by capturing the structure of objects.
Deep Learning of Part-based Representation of Data Using Sparse Autoencoders with Nonnegativity Constraints
We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (NCAE), that learns features which show part-based representation of data.
Similarity-based Text Recognition by Deeply Supervised Siamese Network
In this paper, we propose a new text recognition model based on measuring the visual similarity of text and predicting the content of unlabeled texts.
