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Thanks to the size of these datasets, the associated text comprehension\ntask is well suited for deep-learning techniques that currently seem to\noutperform all alternative approaches. We present a new, simple model that uses\nattention to directly pick the answer from the context as opposed to computing\nthe answer using a blended representation of words in the document as is usual\nin similar models. This makes the model particularly suitable for\nquestion-answering problems where the answer is a single word from the\ndocument. Ensemble of our models sets new state of the art on all evaluated\ndatasets.","url_abs":"http://arxiv.org/abs/1603.01547v2","url_pdf":"http://arxiv.org/pdf/1603.01547v2.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":"text-understanding-with-the-attention-sum","repo_url":"https://github.com/rkadlec/asreader","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"text-understanding-with-the-attention-sum","repo_url":"https://github.com/libertatis/mrc-cbt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"machine-reading-comprehension","task_name":"Machine Reading Comprehension"},{"task_slug":"open-domain-question-answering","task_name":"Open-Domain Question Answering"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/open-domain-question-answering-on-searchqa","task":"Open-Domain Question Answering","dataset":"SearchQA","model":"ASR","rank_in_archive_order":13,"of":14,"metrics":{"N-gram F1":"22.8","Unigram Acc":"41.3"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-cnn-daily-mail","task":"Question Answering","dataset":"CNN / Daily Mail","model":"AS Reader (ensemble model)","rank_in_archive_order":6,"of":16,"metrics":{"CNN":"75.4","Daily Mail":"77.7"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-cnn-daily-mail","task":"Question Answering","dataset":"CNN / Daily Mail","model":"AS Reader (single model)","rank_in_archive_order":12,"of":16,"metrics":{"CNN":" 69.5","Daily Mail":"73.9"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-childrens-book-test","task":"Question Answering","dataset":"Children's Book Test","model":"AS reader (avg)","rank_in_archive_order":5,"of":8,"metrics":{"Accuracy-CN":"68.9%","Accuracy-NE":"70.6%"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-childrens-book-test","task":"Question Answering","dataset":"Children's Book Test","model":"AS reader (greedy)","rank_in_archive_order":6,"of":8,"metrics":{"Accuracy-CN":"67.5%","Accuracy-NE":"71%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.01547","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1603.01547"}},"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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