{"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/contrastive-self-supervised-learning-for","title":"Contrastive Self-Supervised Learning for Commonsense Reasoning","arxiv_id":"2005.00669","date":"2020-05-02","proceeding":"ACL 2020 6","authors":["Tassilo Klein","Moin Nabi"],"abstract":"We propose a self-supervised method to solve Pronoun Disambiguation and Winograd Schema Challenge problems. Our approach exploits the characteristic structure of training corpora related to so-called \"trigger\" words, which are responsible for flipping the answer in pronoun disambiguation. We achieve such commonsense reasoning by constructing pair-wise contrastive auxiliary predictions. To this end, we leverage a mutual exclusive loss regularized by a contrastive margin. Our architecture is based on the recently introduced transformer networks, BERT, that exhibits strong performance on many NLP benchmarks. Empirical results show that our method alleviates the limitation of current supervised approaches for commonsense reasoning. This study opens up avenues for exploiting inexpensive self-supervision to achieve performance gain in commonsense reasoning tasks.","url_abs":"https://arxiv.org/abs/2005.00669v1","url_pdf":"https://arxiv.org/pdf/2005.00669v1.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":"contrastive-self-supervised-learning-for","repo_url":"https://github.com/SAP-samples/acl2020-commonsense","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"contrastive-self-supervised-learning-for","repo_url":"https://github.com/SAP-samples/acl2019-commonsense","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"contrastive-self-supervised-learning-for","repo_url":"https://github.com/SAP-samples/acl2019-commonsense-reasoning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2005.00669","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.00669"}},"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/SAP-samples/acl2019-commonsense","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/SAP-samples/acl2020-commonsense","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/SAP-samples/acl2019-commonsense-reasoning","reach":null}],"summary":{"ran_fixture":1,"ran":1,"unverified":6},"by_repo_kind":{"official":{"samples":3,"ran":0,"repositories":1},"listed":{"samples":5,"ran":2,"repositories":2}},"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":"c5dccfcc7d0ce914","entry":"computeMaximumAttentionScore","repo":"SAP-samples/acl2019-commonsense-reasoning","repo_kind":"listed","path":"commonsense.py","file_url":"https://github.com/SAP-samples/acl2019-commonsense-reasoning/blob/HEAD/commonsense.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c5dccfcc7d0ce914"}},{"code_sha256_prefix":"bd63106a0416368c","entry":"find_sub_list","repo":"SAP-samples/acl2019-commonsense","repo_kind":"listed","path":"MAS.py","file_url":"https://github.com/SAP-samples/acl2019-commonsense/blob/HEAD/MAS.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"bd63106a0416368c"}},{"code_sha256_prefix":"0c8ae0785f89db7b","entry":"MAS","repo":"SAP-samples/acl2019-commonsense","repo_kind":"listed","path":"MAS.py","file_url":"https://github.com/SAP-samples/acl2019-commonsense/blob/HEAD/MAS.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"0c8ae0785f89db7b"}},{"code_sha256_prefix":"ff87d20ae15918c9","entry":"compute_accuracy","repo":"SAP-samples/acl2020-commonsense","repo_kind":"official","path":"scorer.py","file_url":"https://github.com/SAP-samples/acl2020-commonsense/blob/HEAD/scorer.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"ff87d20ae15918c9"}},{"code_sha256_prefix":"0f32d4ee5a8aae9f","entry":"compute_f1","repo":"SAP-samples/acl2020-commonsense","repo_kind":"official","path":"scorer.py","file_url":"https://github.com/SAP-samples/acl2020-commonsense/blob/HEAD/scorer.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"0f32d4ee5a8aae9f"}},{"code_sha256_prefix":"903f94fb6e6bb919","entry":"contains_word","repo":"SAP-samples/acl2019-commonsense","repo_kind":"listed","path":"commonsense.py","file_url":"https://github.com/SAP-samples/acl2019-commonsense/blob/HEAD/commonsense.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"903f94fb6e6bb919"}},{"code_sha256_prefix":"54f76feabdf08c8b","entry":"format_attention","repo":"SAP-samples/acl2019-commonsense","repo_kind":"listed","path":"MAS.py","file_url":"https://github.com/SAP-samples/acl2019-commonsense/blob/HEAD/MAS.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"54f76feabdf08c8b"}},{"code_sha256_prefix":"2a4033e3885367dd","entry":"scorer","repo":"SAP-samples/acl2020-commonsense","repo_kind":"official","path":"scorer.py","file_url":"https://github.com/SAP-samples/acl2020-commonsense/blob/HEAD/scorer.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"2a4033e3885367dd"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}