{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-natural-language-decathlon-multitask","title":"The Natural Language Decathlon: Multitask Learning as Question Answering","arxiv_id":"1806.08730","date":"2018-06-20","proceeding":"ICLR 2019 5","authors":["Bryan McCann","Nitish Shirish Keskar","Caiming Xiong","Richard Socher"],"abstract":"Deep learning has improved performance on many natural language processing\n(NLP) tasks individually. However, general NLP models cannot emerge within a\nparadigm that focuses on the particularities of a single metric, dataset, and\ntask. We introduce the Natural Language Decathlon (decaNLP), a challenge that\nspans ten tasks: question answering, machine translation, summarization,\nnatural language inference, sentiment analysis, semantic role labeling,\nzero-shot relation extraction, goal-oriented dialogue, semantic parsing, and\ncommonsense pronoun resolution. We cast all tasks as question answering over a\ncontext. Furthermore, we present a new Multitask Question Answering Network\n(MQAN) jointly learns all tasks in decaNLP without any task-specific modules or\nparameters in the multitask setting. MQAN shows improvements in transfer\nlearning for machine translation and named entity recognition, domain\nadaptation for sentiment analysis and natural language inference, and zero-shot\ncapabilities for text classification. We demonstrate that the MQAN's\nmulti-pointer-generator decoder is key to this success and performance further\nimproves with an anti-curriculum training strategy. Though designed for\ndecaNLP, MQAN also achieves state of the art results on the WikiSQL semantic\nparsing task in the single-task setting. We also release code for procuring and\nprocessing data, training and evaluating models, and reproducing all\nexperiments for decaNLP.","url_abs":"http://arxiv.org/abs/1806.08730v1","url_pdf":"http://arxiv.org/pdf/1806.08730v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-natural-language-decathlon-multitask","repo_url":"https://github.com/salesforce/decaNLP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"the-natural-language-decathlon-multitask","repo_url":"https://github.com/cheng-Ye/daguan-competition-master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"the-natural-language-decathlon-multitask","repo_url":"https://github.com/eric-erki/decaNLP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"the-natural-language-decathlon-multitask","repo_url":"https://github.com/shishir-roy/decaNLP-with-BERT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"the-natural-language-decathlon-multitask","repo_url":"https://github.com/sigtyp/ST2024","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"the-natural-language-decathlon-multitask","repo_url":"https://github.com/stanford-oval/genienlp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"},{"task_slug":"semantic-role-labeling","task_name":"Semantic Role Labeling"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[{"slug":"decanlp","name":"decaNLP","full_name":"Natural Language Decathlon Benchmark"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.08730","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.08730"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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