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Found 8 papers, 1 papers with code
FPI: Failure Point Isolation in Large-scale Conversational Assistants
Large-scale conversational assistants such as Cortana, Alexa, Google Assistant and Siri process requests through a series of modules for wake word detection, speech recognition, language understanding and response generation.
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9. 3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system.
Cross-Lingual Natural Language Inference
intent-classification
+5
FashionVLP: Vision Language Transformer for Fashion Retrieval With Feedback
Fashion image retrieval based on a query pair of reference image and natural language feedback is a challenging task that requires models to assess fashion related information from visual and textual modalities simultaneously.
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.
Ranked #97 on
Image Classification
on ObjectNet
(using extra training data)
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.
