{"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/conversing-by-reading-contentful-neural","title":"Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading","arxiv_id":"1906.02738","date":"2019-06-06","proceeding":"ACL 2019 7","authors":["Lianhui Qin","Michel Galley","Chris Brockett","Xiaodong Liu","Xiang Gao","Bill Dolan","Yejin Choi","Jianfeng Gao"],"abstract":"Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous. We present a new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused integration of external knowledge than has been possible in prior approaches. To support further research on knowledge-grounded conversation, we introduce a new large-scale conversation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more contentful responses compared to a variety of previous methods, improving both the informativeness and diversity of generated output.","url_abs":"https://arxiv.org/abs/1906.02738v2","url_pdf":"https://arxiv.org/pdf/1906.02738v2.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":"conversing-by-reading-contentful-neural","repo_url":"https://github.com/qkaren/converse_reading_cmr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"informativeness","task_name":"Informativeness"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"},{"task_slug":"response-generation","task_name":"Response Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1906.02738","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.02738"}},"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. 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/qkaren/converse_reading_cmr","reach":null}],"summary":{"ran_draft_wrong":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":"695aa5d2a378108f","entry":"get_domain","repo":"qkaren/converse_reading_cmr","repo_kind":"official","path":"data/src/create_official_data.py","file_url":"https://github.com/qkaren/converse_reading_cmr/blob/HEAD/data/src/create_official_data.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"695aa5d2a378108f"}},{"code_sha256_prefix":"0b9dd235f67c7ead","entry":"get_subreddit","repo":"qkaren/converse_reading_cmr","repo_kind":"official","path":"data/src/create_official_data.py","file_url":"https://github.com/qkaren/converse_reading_cmr/blob/HEAD/data/src/create_official_data.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0b9dd235f67c7ead"}},{"code_sha256_prefix":"2d6234d38ab3c00e","entry":"get_url","repo":"qkaren/converse_reading_cmr","repo_kind":"official","path":"data/src/create_official_data.py","file_url":"https://github.com/qkaren/converse_reading_cmr/blob/HEAD/data/src/create_official_data.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2d6234d38ab3c00e"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}