60 citations · 114 across the 3 of their papers we have counts for
5 papers
PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models
Rabeeh Karimi Mahabadi, Luke Zettlemoyer, James Henderson +4
Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into…
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
Beliz Gunel, Jingfei Du, Alexis Conneau +1
State-of-the-art natural language understanding classification models follow two-stages: pre-training a large language model on an auxiliary task, and then fine-tuning the model on…
Self-training Improves Pre-training for Natural Language Understanding
Jingfei Du, Edouard Grave, Beliz Gunel +5
Unsupervised pre-training has led to much recent progress in natural language understanding. In this paper, we study self-training as another way to leverage unlabeled data through…
Conversational Semantic Parsing
Armen Aghajanyan, Jean Maillard, Akshat Shrivastava +8
The structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the repr…
Preserving Integrity in Online Social Networks
Alon Halevy, Cristian Canton Ferrer, Hao Ma +5
Online social networks provide a platform for sharing information and free expression. However, these networks are also used for malicious purposes, such as distributing misinforma…