{"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/learning-to-generate-equitable-text-in","title":"Learning to Generate Equitable Text in Dialogue from Biased Training Data","arxiv_id":"2307.04303","date":"2023-07-10","proceeding":null,"authors":["Anthony Sicilia","Malihe Alikhani"],"abstract":"The ingrained principles of fairness in a dialogue system's decision-making process and generated responses are crucial for user engagement, satisfaction, and task achievement. Absence of equitable and inclusive principles can hinder the formation of common ground, which in turn negatively impacts the overall performance of the system. For example, misusing pronouns in a user interaction may cause ambiguity about the intended subject. Yet, there is no comprehensive study of equitable text generation in dialogue. Aptly, in this work, we use theories of computational learning to study this problem. We provide formal definitions of equity in text generation, and further, prove formal connections between learning human-likeness and learning equity: algorithms for improving equity ultimately reduce to algorithms for improving human-likeness (on augmented data). With this insight, we also formulate reasonable conditions under which text generation algorithms can learn to generate equitable text without any modifications to the biased training data on which they learn. To exemplify our theory in practice, we look at a group of algorithms for the GuessWhat?! visual dialogue game and, using this example, test our theory empirically. Our theory accurately predicts relative-performance of multiple algorithms in generating equitable text as measured by both human and automated evaluation.","url_abs":"https://arxiv.org/abs/2307.04303v1","url_pdf":"https://arxiv.org/pdf/2307.04303v1.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":"learning-to-generate-equitable-text-in","repo_url":"https://github.com/anthonysicilia/equitable-dialogue-acl2023","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"fairness","task_name":"Fairness"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2307.04303","atlas_url":"https://app.syntology.ai/?focus=2307.04303","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.04303"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/anthonysicilia/equitable-dialogue-acl2023","reach":null},{"provenance":"deterministic:regex_extraction","url":"https://github.com/anthonysicilia/equitable-dialogue-2","reach":{"status":"gone","observed_at":"2026-09-17","how":"tree_404+repo_404"}}],"summary":{"ran_violates":3},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"962406f3f315284c","entry":"isfemale","repo":"anthonysicilia/equitable-dialogue-acl2023","repo_kind":"official","path":"evaluation.py","file_url":"https://github.com/anthonysicilia/equitable-dialogue-acl2023/blob/HEAD/evaluation.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"962406f3f315284c"}},{"code_sha256_prefix":"b530ee437afcaee5","entry":"ismale","repo":"anthonysicilia/equitable-dialogue-acl2023","repo_kind":"official","path":"evaluation.py","file_url":"https://github.com/anthonysicilia/equitable-dialogue-acl2023/blob/HEAD/evaluation.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b530ee437afcaee5"}},{"code_sha256_prefix":"621dd42961127ce2","entry":"isprotected","repo":"anthonysicilia/equitable-dialogue-acl2023","repo_kind":"official","path":"evaluation.py","file_url":"https://github.com/anthonysicilia/equitable-dialogue-acl2023/blob/HEAD/evaluation.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"621dd42961127ce2"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}