3.7k citations · 8.3k across the 22 of their papers we have counts for
6 papers · 1 filter
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh +1
Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summariza…
SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders
Peter J. Liu, Yu-An Chung, Jie Ren
We propose an end-to-end neural model for zero-shot abstractive text summarization of paragraphs, and introduce a benchmark task, ROCSumm, based on ROCStories, a subset for which w…
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts +6
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language proc…
Likelihood Ratios for Out-of-Distribution Detection
Jie Ren, Peter J. Liu, Emily Fertig +5
Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution. On such out-of-distri…
Using Ontologies To Improve Performance In Massively Multi-label Prediction Models
Ethan Steinberg, Peter J. Liu
Massively multi-label prediction/classification problems arise in environments like health-care or biology where very precise predictions are useful. One challenge with massively m…
Assessing The Factual Accuracy of Generated Text
Ben Goodrich, Vinay Rao, Mohammad Saleh +1
We propose a model-based metric to estimate the factual accuracy of generated text that is complementary to typical scoring schemes like ROUGE (Recall-Oriented Understudy for Gisti…