Open AccessJournal Article
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel,Noam Shazeer,Adam Roberts,Katherine Lee,Sharan Narang,Michael Matena,Yanqi Zhou,Wei Li,Peter J. Liu +8 more
Reads0
Chats0
TLDR
This article introduced a unified framework that converts all text-based language problems into a text-to-text format and compared pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks.Abstract:
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 processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new ``Colossal Clean Crawled Corpus'', we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.read more
Citations
More filters
Posted Content
Exceeding the Limits of Visual-Linguistic Multi-Task Learning.
TL;DR: In this article, a large-scale multi-task learning (MTL) approach is proposed to solve 1000 unique classification tasks that share similarly-structured input data, comprised of both text and images.
Posted Content
DistIR: An Intermediate Representation and Simulator for Efficient Neural Network Distribution
TL;DR: DistIR as mentioned in this paper is an intermediate representation for distributed DNN computation that is tailored for efficient analyses, such as simulation, which enables automatically identifying the top-performing strategies without having to execute on physical hardware.
Posted Content
BERT Embeddings Can Track Context in Conversational Search.
TL;DR: In this article, a Transformer-based re-ranking method was used to re-rank the answers given the question and the conversational history to provide the most relevant answers.
Posted Content
RoBERTuito: a pre-trained language model for social media text in Spanish.
TL;DR: This article presented RoBERTuito, a pre-trained language model for user-generated content in Spanish, trained on 500 million tweets in Spanish and showed that it outperformed other pre-learned language models for Spanish.