Title: The Natural Language Decathlon: Multitask Learning as Question Answering. Abstract: Deep learning has improved performance on many natural language processing (NLP) tasks individually.
However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, dataset, and task.
We introduce the Natural Language Decathlon (decaNLP), a challenge that spans ten tasks:
question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, relation extraction, goal-oriented dialogue, semantic parsing, and commonsense pronoun resolution.
We cast all tasks as question answering over a context.
Furthermore, we present a new multitask question answering network (MQAN) that jointly learns all tasks in decaNLP without any task-specific modules or parameters more effectively than sequence-to-sequence and reading comprehension baselines.
MQAN shows improvements in transfer learning for machine translation and named entity recognition, domain adaptation for sentiment analysis and natural language inference, and zero-shot capabilities for text classification.
We demonstrate that the MQAN's multi-pointer-generator decoder is key to this success and that performance further improves with an anti-curriculum training strategy.
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting. 
We also release code for procuring and processing data, training and evaluating models, and reproducing all experiments for decaNLP.