Papperoni

2018

The Natural Language Decathlon: Multitask Learning as Question Answering

Richard Socher

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citations

Cite Score

30

AI summary

This paper introduces the Natural Language Decathlon (decaNLP) benchmark comprising 10 NLP tasks cast as question answering and proposes a Multitask Question Answering Network (MQAN) that jointly learns all tasks without task-specific modules, achieving state-of-the-art results on WikiSQL and demonstrating transfer learning and zero-shot capabilities.

Main Contributions

Introduction of the Natural Language Decathlon (decaNLP), a benchmark spanning ten diverse NLP tasks.
Casting all ten NLP tasks as question answering over a context for unified modeling.
Presentation of MQAN, a multitask question answering network, capable of jointly learning all decaNLP tasks without task-specific modules or parameters.
Demonstration of MQAN's effectiveness in transfer learning, domain adaptation, and zero-shot capabilities.
Achieving state-of-the-art results on the WikiSQL semantic parsing task in a single-task setting, despite MQAN not being specifically designed for it.

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. 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.

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References [128]

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on November 12, 2025

Very nice results, but TBH the architecture is waaaay to complicated

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