12 citations · 13 across the 2 of their papers we have counts for
3 papers · 1 filter
Efficient Few-Shot Fine-Tuning for Opinion Summarization
Arthur Bražinskas, Ramesh Nallapati, Mohit Bansal +1
Abstractive summarization models are typically pre-trained on large amounts of generic texts, then fine-tuned on tens or hundreds of thousands of annotated samples. However, in opi…
Multi-Task Networks With Universe, Group, and Task Feature Learning
Shiva Pentyala, Mengwen Liu, Markus Dreyer
We present methods for multi-task learning that take advantage of natural groupings of related tasks. Task groups may be defined along known properties of the tasks, such as task d…
Transfer Learning for Neural Semantic Parsing
Xing Fan, Emilio Monti, Lambert Mathias +1
The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL). One of the constraints that limits full exploration of dee…