{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/relation-classification/papers/ran/1","list_of":"/task/relation-classification","task":"Relation Classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,29],"of":29,"counts":{"archive_papers_tagged":445,"with_a_code_link":160,"where_syntology_ran_a_sample":29,"not_listed_spam_title":0,"listed":445,"listed_where_code_ran":29,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":25,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":25,"listed_every_run_a_failure_of_syntologys_instrument":4,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/relation-classification/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/will-llms-replace-the-encoder-only-models-in","slug":"will-llms-replace-the-encoder-only-models-in","title":"Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?","date":"2024-10-14","arxiv_id":"2410.10476","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/will-llms-replace-the-encoder-only-models-in#ran","syntology_url":"https://syntology.ai/paper/2410.10476","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.10476"}},"official":{"repos":["brownfortress/llms-trc"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/oed-towards-one-stage-end-to-end-dynamic","slug":"oed-towards-one-stage-end-to-end-dynamic","title":"OED: Towards One-stage End-to-End Dynamic Scene Graph Generation","date":"2024-05-27","arxiv_id":"2405.16925","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/oed-towards-one-stage-end-to-end-dynamic#ran","syntology_url":"https://syntology.ai/paper/2405.16925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16925"}},"official":{"repos":["guanw-pku/oed"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/knowledgehub-an-end-to-end-tool-for-assisted","slug":"knowledgehub-an-end-to-end-tool-for-assisted","title":"KnowledgeHub: An end-to-end Tool for Assisted Scientific Discovery","date":"2024-05-16","arxiv_id":"2406.00008","repositories_listed":0,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/knowledgehub-an-end-to-end-tool-for-assisted#ran","syntology_url":"https://syntology.ai/paper/2406.00008","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.00008"}},"official":null}},{"url":"/paper/rexel-an-end-to-end-model-for-document-level","slug":"rexel-an-end-to-end-model-for-document-level","title":"REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking","date":"2024-04-19","arxiv_id":"2404.12788","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rexel-an-end-to-end-model-for-document-level#ran","syntology_url":"https://syntology.ai/paper/2404.12788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.12788"}},"official":{"repos":["amazon-science/e2e-docie"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/graph-language-models","slug":"graph-language-models","title":"Graph Language Models","date":"2024-01-13","arxiv_id":"2401.07105","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/graph-language-models#ran","syntology_url":"https://syntology.ai/paper/2401.07105","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.07105"}},"official":{"repos":["heidelberg-nlp/graphlanguagemodels"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/core-a-few-shot-company-relation","slug":"core-a-few-shot-company-relation","title":"CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation","date":"2023-10-18","arxiv_id":"2310.12024","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/core-a-few-shot-company-relation#ran","syntology_url":"https://syntology.ai/paper/2310.12024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.12024"}},"official":{"repos":["pnborchert/core"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/rationale-enhanced-language-models-are-better","slug":"rationale-enhanced-language-models-are-better","title":"Rationale-Enhanced Language Models are Better Continual Relation Learners","date":"2023-10-10","arxiv_id":"2310.06547","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rationale-enhanced-language-models-are-better#ran","syntology_url":"https://syntology.ai/paper/2310.06547","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.06547"}},"official":{"repos":["weiminxiong/rationalecl"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/annotation-inspired-implicit-discourse","slug":"annotation-inspired-implicit-discourse","title":"Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation","date":"2023-06-10","arxiv_id":"2306.06480","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/annotation-inspired-implicit-discourse#ran","syntology_url":"https://syntology.ai/paper/2306.06480","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.06480"}},"official":{"repos":["liuwei1206/connrel"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/matsci-nlp-evaluating-scientific-language","slug":"matsci-nlp-evaluating-scientific-language","title":"MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling","date":"2023-05-14","arxiv_id":"2305.08264","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/matsci-nlp-evaluating-scientific-language#ran","syntology_url":"https://syntology.ai/paper/2305.08264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.08264"}},"official":{"repos":["banglab-udem-mila/nlp4matsci-acl23"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/vector-quantized-input-contextualized-soft","slug":"vector-quantized-input-contextualized-soft","title":"Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding","date":"2022-05-23","arxiv_id":"2205.11024","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/vector-quantized-input-contextualized-soft#ran","syntology_url":"https://syntology.ai/paper/2205.11024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.11024"}},"official":{"repos":["declare-lab/vip"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deepstruct-pretraining-of-language-models-for-1","slug":"deepstruct-pretraining-of-language-models-for-1","title":"DeepStruct: Pretraining of Language Models for Structure Prediction","date":"2022-05-21","arxiv_id":"2205.10475","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":9,"n_pointer_only":3,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deepstruct-pretraining-of-language-models-for-1#ran","syntology_url":"https://syntology.ai/paper/2205.10475","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10475"}},"official":{"repos":["cgraywang/deepstruct"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unified-semantic-typing-with-meaningful-label","slug":"unified-semantic-typing-with-meaningful-label","title":"Unified Semantic Typing with Meaningful Label Inference","date":"2022-05-04","arxiv_id":"2205.01826","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unified-semantic-typing-with-meaningful-label#ran","syntology_url":"https://syntology.ai/paper/2205.01826","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.01826"}},"official":{"repos":["luka-group/unist"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/relationprompt-leveraging-prompts-to-generate","slug":"relationprompt-leveraging-prompts-to-generate","title":"RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction","date":"2022-03-17","arxiv_id":"2203.09101","repositories_listed":2,"syntology":{"n":26,"n_ran":15,"n_constructed":1,"n_ran_checked":7,"n_instrument":8,"n_unverified":11,"n_honours":4,"n_violates":0,"n_no_contract":3,"n_pointer_only":16,"phrase":"15 ran (of which 1 constructed an object rather than computing a result; 7 with no instrument failure: 4 honoured, 0 violated, 3 with no contract checked; 8 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/relationprompt-leveraging-prompts-to-generate#ran","syntology_url":"https://syntology.ai/paper/2203.09101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09101"}},"official":{"repos":["declare-lab/relationprompt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":6,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/commonsense-knowledge-augmented-pretrained-1","slug":"commonsense-knowledge-augmented-pretrained-1","title":"Knowledge-Augmented Language Models for Cause-Effect Relation Classification","date":"2021-12-16","arxiv_id":"2112.08615","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/commonsense-knowledge-augmented-pretrained-1#ran","syntology_url":"https://syntology.ai/paper/2112.08615","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.08615"}},"official":{"repos":["phosseini/causal-reasoning"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sornet-spatial-object-centric-representations","slug":"sornet-spatial-object-centric-representations","title":"SORNet: Spatial Object-Centric Representations for Sequential Manipulation","date":"2021-09-08","arxiv_id":"2109.03891","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/sornet-spatial-object-centric-representations#ran","syntology_url":"https://syntology.ai/paper/2109.03891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.03891"}},"official":{"repos":["wentaoyuan/sornet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unire-a-unified-label-space-for-entity","slug":"unire-a-unified-label-space-for-entity","title":"UniRE: A Unified Label Space for Entity Relation Extraction","date":"2021-07-09","arxiv_id":"2107.04292","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unire-a-unified-label-space-for-entity#ran","syntology_url":"https://syntology.ai/paper/2107.04292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.04292"}},"official":{"repos":["Receiling/UniRE"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ptr-prompt-tuning-with-rules-for-text","slug":"ptr-prompt-tuning-with-rules-for-text","title":"PTR: Prompt Tuning with Rules for Text Classification","date":"2021-05-24","arxiv_id":"2105.11259","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ptr-prompt-tuning-with-rules-for-text#ran","syntology_url":"https://syntology.ai/paper/2105.11259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.11259"}},"official":{"repos":["thunlp/PTR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/luke-deep-contextualized-entity","slug":"luke-deep-contextualized-entity","title":"LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention","date":"2020-10-02","arxiv_id":"2010.01057","repositories_listed":9,"syntology":{"n":10,"n_ran":3,"n_constructed":2,"n_ran_checked":3,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/luke-deep-contextualized-entity#ran","syntology_url":"https://syntology.ai/paper/2010.01057","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.01057"}},"official":{"repos":["studio-ousia/luke"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/k-adapter-infusing-knowledge-into-pre-trained","slug":"k-adapter-infusing-knowledge-into-pre-trained","title":"K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters","date":"2020-02-05","arxiv_id":"2002.01808","repositories_listed":2,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/k-adapter-infusing-knowledge-into-pre-trained#ran","syntology_url":"https://syntology.ai/paper/2002.01808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.01808"}},"official":null}},{"url":"/paper/kepler-a-unified-model-for-knowledge","slug":"kepler-a-unified-model-for-knowledge","title":"KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation","date":"2019-11-13","arxiv_id":"1911.06136","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/kepler-a-unified-model-for-knowledge#ran","syntology_url":"https://syntology.ai/paper/1911.06136","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.06136"}},"official":{"repos":["THU-KEG/KEPLER"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fewrel-20-towards-more-challenging-few-shot","slug":"fewrel-20-towards-more-challenging-few-shot","title":"FewRel 2.0: Towards More Challenging Few-Shot Relation Classification","date":"2019-10-16","arxiv_id":"1910.07124","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fewrel-20-towards-more-challenging-few-shot#ran","syntology_url":"https://syntology.ai/paper/1910.07124","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.07124"}},"official":{"repos":["thunlp/fewrel"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-text-classification-with","slug":"few-shot-text-classification-with","title":"Few-shot Text Classification with Distributional Signatures","date":"2019-08-16","arxiv_id":"1908.06039","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/few-shot-text-classification-with#ran","syntology_url":"https://syntology.ai/paper/1908.06039","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.06039"}},"official":{"repos":["YujiaBao/Distributional-Signatures"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/spanbert-improving-pre-training-by","slug":"spanbert-improving-pre-training-by","title":"SpanBERT: Improving Pre-training by Representing and Predicting Spans","date":"2019-07-24","arxiv_id":"1907.10529","repositories_listed":6,"syntology":{"n":15,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/spanbert-improving-pre-training-by#ran","syntology_url":"https://syntology.ai/paper/1907.10529","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.10529"}},"official":{"repos":["facebookresearch/SpanBERT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/matching-the-blanks-distributional-similarity","slug":"matching-the-blanks-distributional-similarity","title":"Matching the Blanks: Distributional Similarity for Relation Learning","date":"2019-06-07","arxiv_id":"1906.03158","repositories_listed":13,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/matching-the-blanks-distributional-similarity#ran","syntology_url":"https://syntology.ai/paper/1906.03158","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.03158"}},"official":null}},{"url":"/paper/enriching-pre-trained-language-model-with","slug":"enriching-pre-trained-language-model-with","title":"Enriching Pre-trained Language Model with Entity Information for Relation Classification","date":"2019-05-20","arxiv_id":"1905.08284","repositories_listed":6,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/enriching-pre-trained-language-model-with#ran","syntology_url":"https://syntology.ai/paper/1905.08284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.08284"}},"official":null}},{"url":"/paper/ernie-enhanced-language-representation-with","slug":"ernie-enhanced-language-representation-with","title":"ERNIE: Enhanced Language Representation with Informative Entities","date":"2019-05-17","arxiv_id":"1905.07129","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ernie-enhanced-language-representation-with#ran","syntology_url":"https://syntology.ai/paper/1905.07129","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.07129"}},"official":{"repos":["thunlp/ERNIE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/advancing-nlp-with-cognitive-language","slug":"advancing-nlp-with-cognitive-language","title":"Advancing NLP with Cognitive Language Processing Signals","date":"2019-04-04","arxiv_id":"1904.02682","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/advancing-nlp-with-cognitive-language#ran","syntology_url":"https://syntology.ai/paper/1904.02682","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02682"}},"official":{"repos":["DS3Lab/ner-at-first-sight","DS3Lab/zuco-nlp"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fewrel-a-large-scale-supervised-few-shot","slug":"fewrel-a-large-scale-supervised-few-shot","title":"FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation","date":"2018-10-24","arxiv_id":"1810.10147","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fewrel-a-large-scale-supervised-few-shot#ran","syntology_url":"https://syntology.ai/paper/1810.10147","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.10147"}},"official":null}},{"url":"/paper/classifying-relations-by-ranking-with","slug":"classifying-relations-by-ranking-with","title":"Classifying Relations by Ranking with Convolutional Neural Networks","date":"2015-04-24","arxiv_id":"1504.06580","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/classifying-relations-by-ranking-with#ran","syntology_url":"https://syntology.ai/paper/1504.06580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1504.06580"}},"official":null}}],"record_sha256":"cf169388ca9d30cbef3bcd8a4040ef340abbc999ce69ba9c7a43793b56e3e0c2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}