{"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/evidence-aware-inferential-text-generation","title":"Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder","arxiv_id":"2006.08101","date":"2020-06-15","proceeding":"ACL 2020 6","authors":["Daya Guo","Duyu Tang","Nan Duan","Jian Yin","Daxin Jiang","Ming Zhou"],"abstract":"Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs. Existing works usually ignore the context that is not explicitly provided, resulting in a context-independent semantic representation that struggles to support the generation. To address this, we propose an approach that automatically finds evidence for an event from a large text corpus, and leverages the evidence to guide the generation of inferential texts. Our approach works in an encoder-decoder manner and is equipped with a Vector Quantised-Variational Autoencoder, where the encoder outputs representations from a distribution over discrete variables. Such discrete representations enable automatically selecting relevant evidence, which not only facilitates evidence-aware generation, but also provides a natural way to uncover rationales behind the generation. Our approach provides state-of-the-art performance on both Event2Mind and ATOMIC datasets. More importantly, we find that with discrete representations, our model selectively uses evidence to generate different inferential texts.","url_abs":"https://arxiv.org/abs/2006.08101v1","url_pdf":"https://arxiv.org/pdf/2006.08101v1.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":"evidence-aware-inferential-text-generation","repo_url":"https://github.com/microsoft/EA-VQ-VAE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"common-sense-reasoning","task_name":"Common Sense Reasoning"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/common-sense-reasoning-on-event2mind-test","task":"Common Sense Reasoning","dataset":"Event2Mind test","model":"EA-VQ-VAE","rank_in_archive_order":3,"of":7,"metrics":{"BLEU":"11.31"},"uses_additional_data":false},{"leaderboard":"/sota/common-sense-reasoning-on-event2mind-test","task":"Common Sense Reasoning","dataset":"Event2Mind test","model":"COMET*","rank_in_archive_order":4,"of":7,"metrics":{"BLEU":"10.37"},"uses_additional_data":false},{"leaderboard":"/sota/common-sense-reasoning-on-event2mind-test","task":"Common Sense Reasoning","dataset":"Event2Mind test","model":"S2S*","rank_in_archive_order":5,"of":7,"metrics":{"BLEU":"9.43"},"uses_additional_data":false},{"leaderboard":"/sota/common-sense-reasoning-on-event2mind-test","task":"Common Sense Reasoning","dataset":"Event2Mind test","model":"CWVAE","rank_in_archive_order":6,"of":7,"metrics":{"BLEU":"8.53"},"uses_additional_data":false},{"leaderboard":"/sota/common-sense-reasoning-on-event2mind-test","task":"Common Sense Reasoning","dataset":"Event2Mind test","model":"VRNMT","rank_in_archive_order":7,"of":7,"metrics":{"BLEU":"4.03"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.08101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.08101"}},"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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