3 citations · 5 across the 4 of their papers we have counts for
7 papers
Label Semantic Aware Pre-training for Few-shot Text Classification
Aaron Mueller, Jason Krone, Salvatore Romeo +4
In text classification tasks, useful information is encoded in the label names. Label semantic aware systems have leveraged this information for improved text classification perfor…
Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer
Weijia Xu, Batool Haider, Jason Krone +1
Multilingual pre-trained contextual embedding models (Devlin et al., 2019) have achieved impressive performance on zero-shot cross-lingual transfer tasks. Finding the most effectiv…
On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise
Sailik Sengupta, Jason Krone, Saab Mansour
Intent Classification (IC) and Slot Labeling (SL) models, which form the basis of dialogue systems, often encounter noisy data in real-word environments. In this work, we investiga…
Meta learning to classify intent and slot labels with noisy few shot examples
Shang-Wen Li, Jason Krone, Shuyan Dong +2
Recently deep learning has dominated many machine learning areas, including spoken language understanding (SLU). However, deep learning models are notorious for being data-hungry,…
Augmented Natural Language for Generative Sequence Labeling
Ben Athiwaratkun, Cicero Nogueira dos Santos, Jason Krone +1
We propose a generative framework for joint sequence labeling and sentence-level classification. Our model performs multiple sequence labeling tasks at once using a single, shared…
Learning to Classify Intents and Slot Labels Given a Handful of Examples
Jason Krone, Yi Zhang, Mona Diab
Intent classification (IC) and slot filling (SF) are core components in most goal-oriented dialogue systems. Current IC/SF models perform poorly when the number of training example…