{"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-dual-retrieval-module-for-semi","title":"Learning Dual Retrieval Module for Semi-supervised Relation Extraction","arxiv_id":"1902.07814","date":"2019-02-20","proceeding":null,"authors":["Hongtao Lin","Jun Yan","Meng Qu","Xiang Ren"],"abstract":"Relation extraction is an important task in structuring content of text data,\nand becomes especially challenging when learning with weak supervision---where\nonly a limited number of labeled sentences are given and a large number of\nunlabeled sentences are available. Most existing work exploits unlabeled data\nbased on the ideas of self-training (i.e., bootstrapping a model) and\nmulti-view learning (e.g., ensembling multiple model variants). However, these\nmethods either suffer from the issue of semantic drift, or do not fully capture\nthe problem characteristics of relation extraction. In this paper, we leverage\na key insight that retrieving sentences expressing a relation is a dual task of\npredicting relation label for a given sentence---two tasks are complementary to\neach other and can be optimized jointly for mutual enhancement. To model this\nintuition, we propose DualRE, a principled framework that introduces a\nretrieval module which is jointly trained with the original relation prediction\nmodule. In this way, high-quality samples selected by retrieval module from\nunlabeled data can be used to improve prediction module, and vice versa.\nExperimental results\\footnote{\\small Code and data can be found at\n\\url{https://github.com/INK-USC/DualRE}.} on two public datasets as well as\ncase studies demonstrate the effectiveness of the DualRE approach.","url_abs":"http://arxiv.org/abs/1902.07814v2","url_pdf":"http://arxiv.org/pdf/1902.07814v2.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-dual-retrieval-module-for-semi","repo_url":"https://github.com/INK-USC/DualRE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"multi-view-learning","task_name":"MULTI-VIEW LEARNING"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"relation-prediction","task_name":"Relation Prediction"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.07814","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.07814"}},"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/INK-USC/DualRE","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":3,"samples":[{"code_sha256_prefix":"b0cf1868b1c9f56f","entry":"convert_tacred_format","repo":"INK-USC/DualRE","repo_kind":"official","path":"utils/data_utils.py","file_url":"https://github.com/INK-USC/DualRE/blob/HEAD/utils/data_utils.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":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b0cf1868b1c9f56f"}},{"code_sha256_prefix":"ef2bb9f15dc13608","entry":"get_pst","repo":"INK-USC/DualRE","repo_kind":"official","path":"utils/data_utils.py","file_url":"https://github.com/INK-USC/DualRE/blob/HEAD/utils/data_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ef2bb9f15dc13608"}},{"code_sha256_prefix":"cfdea81e0b0366a4","entry":"mask_tokens","repo":"INK-USC/DualRE","repo_kind":"official","path":"utils/data_utils.py","file_url":"https://github.com/INK-USC/DualRE/blob/HEAD/utils/data_utils.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":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cfdea81e0b0366a4"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}