Search Results for author:
Found 5 papers, 0 papers with code
Error Detection in Large-Scale Natural Language Understanding Systems Using Transformer Models
We tested our approach for detecting misclassifications from one domain that accounts for <0. 5% of the traffic in a large-scale conversational AI system.
FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models
On the multilingual TydiQA benchmark, our model outperforms the XLM-Roberta-large by an absolute margin of upto 40 F1 points and an average of 33 F1 points in a few-shot setting (<= 64 training examples).
Style-Aware Normalized Loss for Improving Arbitrary Style Transfer
Neural Style Transfer (NST) has quickly evolved from single-style to infinite-style models, also known as Arbitrary Style Transfer (AST).
Class-agnostic Object Detection
Finally, we propose (1) baseline methods and (2) a new adversarial learning framework for class-agnostic detection that forces the model to exclude class-specific information from features used for predictions.
Zero-shot Event Detection using Multi-modal Fusion of Weakly Supervised Concepts
Current state-of-the-art systems for visual content analysis require large training sets for each class of interest, and performance degrades rapidly with fewer examples.