Equip** machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational
facts from large corpora. The technique well suits many open-world natural language …

This paper explores a simple method for improving the zero-shot learning abilities of
language models. We show that instruction tuning--finetuning language models on a …

We introduce MetaICL (Meta-training for In-Context Learning), a new meta-training
framework for few-shot learning where a pretrained language model is tuned to do in …

Deep learning has improved performance on many natural language processing (NLP)
tasks individually. However, general NLP models cannot emerge within a paradigm that …

This paper describes AllenNLP, a platform for research on deep learning methods in natural
language understanding. AllenNLP is designed to support researchers who want to build …

While pretrained models such as BERT have shown large gains across natural language
understanding tasks, their performance can be improved by further training the model on a …

We introduce a method for improving the structural understanding abilities of language
models. Unlike previous approaches that finetune the models with task-specific …

We show that relation extraction can be reduced to answering simple reading
comprehension questions, by associating one or more natural-language questions with …

Humans can learn a new language task efficiently with only few examples, by leveraging
their knowledge obtained when learning prior tasks. In this paper, we explore whether and …