{"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/translation/papers/16","list_of":"/task/translation","task":"Translation","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":16,"pages_in_order":124,"rows_per_page":100,"rows":[1501,1600],"of":12395,"counts":{"archive_papers_tagged":12395,"with_a_code_link":3574,"where_syntology_ran_a_sample":682,"not_listed_spam_title":0,"listed":12395,"listed_where_code_ran":682,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":566,"every_run_a_failure_of_syntologys_instrument":116,"listed_with_a_run_with_no_instrument_failure":566,"listed_every_run_a_failure_of_syntologys_instrument":116,"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/translation","prev":"/task/translation/papers/15","next":"/task/translation/papers/17","papers":[{"url":"/paper/phrasetransformer-an-incorporation-of-local","slug":"phrasetransformer-an-incorporation-of-local","title":"PhraseTransformer: An Incorporation of Local Context Information into Sequence-to-sequence Semantic Parsing","date":"2022-11-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/soft-alignment-objectives-for-robust","slug":"soft-alignment-objectives-for-robust","title":"Soft Alignment Objectives for Robust Adaptation of Language Generation","date":"2022-11-29","arxiv_id":"2211.16550","repositories_listed":1,"syntology":null},{"url":"/paper/unified-discrete-diffusion-for-simultaneous","slug":"unified-discrete-diffusion-for-simultaneous","title":"Unified Discrete Diffusion for Simultaneous Vision-Language Generation","date":"2022-11-27","arxiv_id":"2211.14842","repositories_listed":1,"syntology":null},{"url":"/paper/competency-aware-neural-machine-translation","slug":"competency-aware-neural-machine-translation","title":"Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?","date":"2022-11-25","arxiv_id":"2211.13865","repositories_listed":1,"syntology":null},{"url":"/paper/torchscale-transformers-at-scale","slug":"torchscale-transformers-at-scale","title":"TorchScale: Transformers at Scale","date":"2022-11-23","arxiv_id":"2211.13184","repositories_listed":1,"syntology":null},{"url":"/paper/mariancg-a-code-generation-transformer-model","slug":"mariancg-a-code-generation-transformer-model","title":"MarianCG: a code generation transformer model inspired by machine translation","date":"2022-11-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pliks-a-pseudo-linear-inverse-kinematic","slug":"pliks-a-pseudo-linear-inverse-kinematic","title":"PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation","date":"2022-11-21","arxiv_id":"2211.11734","repositories_listed":1,"syntology":null},{"url":"/paper/a-theory-of-unsupervised-translation-1","slug":"a-theory-of-unsupervised-translation-1","title":"A Theory of Unsupervised Translation Motivated by Understanding Animal Communication","date":"2022-11-20","arxiv_id":"2211.11081","repositories_listed":1,"syntology":null},{"url":"/paper/constraining-multi-scale-pairwise-features","slug":"constraining-multi-scale-pairwise-features","title":"Rethinking the Paradigm of Content Constraints in Unpaired Image-to-Image Translation","date":"2022-11-20","arxiv_id":"2211.10867","repositories_listed":1,"syntology":null},{"url":"/paper/a-copy-mechanism-for-handling-knowledge-base","slug":"a-copy-mechanism-for-handling-knowledge-base","title":"A Copy Mechanism for Handling Knowledge Base Elements in SPARQL Neural Machine Translation","date":"2022-11-18","arxiv_id":"2211.10271","repositories_listed":1,"syntology":null},{"url":"/paper/dialogs-re-enacted-across-languages","slug":"dialogs-re-enacted-across-languages","title":"Dialogs Re-enacted Across Languages","date":"2022-11-18","arxiv_id":"2211.11584","repositories_listed":1,"syntology":null},{"url":"/paper/mt-metrics-correlate-with-human-ratings-of","slug":"mt-metrics-correlate-with-human-ratings-of","title":"MT Metrics Correlate with Human Ratings of Simultaneous Speech Translation","date":"2022-11-16","arxiv_id":"2211.08633","repositories_listed":1,"syntology":null},{"url":"/paper/squeeze-flow-of-micro-droplets-convolutional","slug":"squeeze-flow-of-micro-droplets-convolutional","title":"Squeeze flow of micro-droplets: convolutional neural network with trainable and tunable refinement","date":"2022-11-16","arxiv_id":"2211.09061","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-phrase-based-sequence-to","slug":"hierarchical-phrase-based-sequence-to","title":"Hierarchical Phrase-based Sequence-to-Sequence Learning","date":"2022-11-15","arxiv_id":"2211.07906","repositories_listed":1,"syntology":null},{"url":"/paper/easy-guided-decoding-in-providing-suggestions","slug":"easy-guided-decoding-in-providing-suggestions","title":"Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation","date":"2022-11-14","arxiv_id":"2211.07093","repositories_listed":1,"syntology":null},{"url":"/paper/lsa-t-the-first-continuous-argentinian-sign","slug":"lsa-t-the-first-continuous-argentinian-sign","title":"LSA-T: The first continuous Argentinian Sign Language dataset for Sign Language Translation","date":"2022-11-14","arxiv_id":"2211.15481","repositories_listed":1,"syntology":null},{"url":"/paper/what-knowledge-is-needed-towards-explainable","slug":"what-knowledge-is-needed-towards-explainable","title":"What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation","date":"2022-11-08","arxiv_id":"2211.04052","repositories_listed":1,"syntology":null},{"url":"/paper/moving-frame-net-se-3-equivariant-network-for","slug":"moving-frame-net-se-3-equivariant-network-for","title":"Moving Frame Net: SE(3)-Equivariant Network for Volumes","date":"2022-11-07","arxiv_id":"2211.03420","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/moving-frame-net-se-3-equivariant-network-for#ran","syntology_url":"https://syntology.ai/paper/2211.03420","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.03420"}},"official":{"repos":["mateussangalli/movingframenetwork"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/continual-learning-of-neural-machine","slug":"continual-learning-of-neural-machine","title":"Continual Learning of Neural Machine Translation within Low Forgetting Risk Regions","date":"2022-11-03","arxiv_id":"2211.01542","repositories_listed":1,"syntology":null},{"url":"/paper/learning-an-artificial-language-for-knowledge","slug":"learning-an-artificial-language-for-knowledge","title":"Learning an Artificial Language for Knowledge-Sharing in Multilingual Translation","date":"2022-11-02","arxiv_id":"2211.01292","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-an-artificial-language-for-knowledge#ran","syntology_url":"https://syntology.ai/paper/2211.01292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.01292"}},"official":{"repos":["dannigt/fairseq"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mt-geneval-a-counterfactual-and-contextual","slug":"mt-geneval-a-counterfactual-and-contextual","title":"MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation","date":"2022-11-02","arxiv_id":"2211.01355","repositories_listed":1,"syntology":null},{"url":"/paper/two-stream-network-for-sign-language","slug":"two-stream-network-for-sign-language","title":"Two-Stream Network for Sign Language Recognition and Translation","date":"2022-11-02","arxiv_id":"2211.01367","repositories_listed":1,"syntology":null},{"url":"/paper/joint-pre-training-with-speech-and-bilingual","slug":"joint-pre-training-with-speech-and-bilingual","title":"Joint Pre-Training with Speech and Bilingual Text for Direct Speech to Speech Translation","date":"2022-10-31","arxiv_id":"2210.17027","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-speech-translation-with-dynamic","slug":"efficient-speech-translation-with-dynamic","title":"Efficient Speech Translation with Dynamic Latent Perceivers","date":"2022-10-28","arxiv_id":"2210.16264","repositories_listed":1,"syntology":null},{"url":"/paper/improving-zero-shot-multilingual-translation","slug":"improving-zero-shot-multilingual-translation","title":"Improving Zero-Shot Multilingual Translation with Universal Representations and Cross-Mappings","date":"2022-10-28","arxiv_id":"2210.15851","repositories_listed":1,"syntology":null},{"url":"/paper/aces-translation-accuracy-challenge-sets-for","slug":"aces-translation-accuracy-challenge-sets-for","title":"ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics","date":"2022-10-27","arxiv_id":"2210.15615","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/aces-translation-accuracy-challenge-sets-for#ran","syntology_url":"https://syntology.ai/paper/2210.15615","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.15615"}},"official":{"repos":["edinburghnlp/aces"],"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"]}}},{"url":"/paper/morphte-injecting-morphology-in-tensorized","slug":"morphte-injecting-morphology-in-tensorized","title":"MorphTE: Injecting Morphology in Tensorized Embeddings","date":"2022-10-27","arxiv_id":"2210.15379","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":1,"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/morphte-injecting-morphology-in-tensorized#ran","syntology_url":"https://syntology.ai/paper/2210.15379","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.15379"}},"official":{"repos":["bigganbing/Fairseq_MorphTE"],"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/a-bilingual-parallel-corpus-with-discourse","slug":"a-bilingual-parallel-corpus-with-discourse","title":"A Bilingual Parallel Corpus with Discourse Annotations","date":"2022-10-26","arxiv_id":"2210.14667","repositories_listed":1,"syntology":null},{"url":"/paper/computer-aided-modelling-of-the-bilingual","slug":"computer-aided-modelling-of-the-bilingual","title":"Computer-Aided Modelling of the Bilingual Word Indices to the Ninth-Century Uchitel'noe evangelie","date":"2022-10-25","arxiv_id":"2211.05579","repositories_listed":1,"syntology":null},{"url":"/paper/demetr-diagnosing-evaluation-metrics-for","slug":"demetr-diagnosing-evaluation-metrics-for","title":"DEMETR: Diagnosing Evaluation Metrics for Translation","date":"2022-10-25","arxiv_id":"2210.13746","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-document-level-literary-machine","slug":"exploring-document-level-literary-machine","title":"Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature","date":"2022-10-25","arxiv_id":"2210.14250","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/exploring-document-level-literary-machine#ran","syntology_url":"https://syntology.ai/paper/2210.14250","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.14250"}},"official":{"repos":["katherinethai/par3"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/bilingual-synchronization-restoring","slug":"bilingual-synchronization-restoring","title":"Bilingual Synchronization: Restoring Translational Relationships with Editing Operations","date":"2022-10-24","arxiv_id":"2210.13163","repositories_listed":1,"syntology":null},{"url":"/paper/does-joint-training-really-help-cascaded","slug":"does-joint-training-really-help-cascaded","title":"Does Joint Training Really Help Cascaded Speech Translation?","date":"2022-10-24","arxiv_id":"2210.13700","repositories_listed":1,"syntology":null},{"url":"/paper/don-t-discard-fixed-window-audio-segmentation","slug":"don-t-discard-fixed-window-audio-segmentation","title":"Don't Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation","date":"2022-10-24","arxiv_id":"2210.13363","repositories_listed":1,"syntology":null},{"url":"/paper/focused-concatenation-for-context-aware","slug":"focused-concatenation-for-context-aware","title":"Focused Concatenation for Context-Aware Neural Machine Translation","date":"2022-10-24","arxiv_id":"2210.13388","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":0,"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/focused-concatenation-for-context-aware#ran","syntology_url":"https://syntology.ai/paper/2210.13388","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13388"}},"official":{"repos":["lorelupo/focused-concat"],"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/unsupervised-object-representation-learning","slug":"unsupervised-object-representation-learning","title":"Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE","date":"2022-10-24","arxiv_id":"2210.12918","repositories_listed":1,"syntology":{"n":20,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/unsupervised-object-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2210.12918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12918"}},"official":{"repos":["smlc-nysbc/target-vae"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/translation-word-level-auto-completion-what","slug":"translation-word-level-auto-completion-what","title":"Translation Word-Level Auto-Completion: What can we achieve out of the box?","date":"2022-10-23","arxiv_id":"2210.12802","repositories_listed":1,"syntology":null},{"url":"/paper/information-transport-based-policy-for","slug":"information-transport-based-policy-for","title":"Information-Transport-based Policy for Simultaneous Translation","date":"2022-10-22","arxiv_id":"2210.12357","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":2,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/information-transport-based-policy-for#ran","syntology_url":"https://syntology.ai/paper/2210.12357","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12357"}},"official":{"repos":["ictnlp/itst"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-textless-metric-for-speech-to-speech","slug":"a-textless-metric-for-speech-to-speech","title":"A Textless Metric for Speech-to-Speech Comparison","date":"2022-10-21","arxiv_id":"2210.11835","repositories_listed":1,"syntology":null},{"url":"/paper/joint-speech-translation-and-named-entity","slug":"joint-speech-translation-and-named-entity","title":"Joint Speech Translation and Named Entity Recognition","date":"2022-10-21","arxiv_id":"2210.11987","repositories_listed":1,"syntology":null},{"url":"/paper/m-4adapter-multilingual-multi-domain","slug":"m-4adapter-multilingual-multi-domain","title":"$m^4Adapter$: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter","date":"2022-10-21","arxiv_id":"2210.11912","repositories_listed":1,"syntology":null},{"url":"/paper/syntax-guided-localized-self-attention-by","slug":"syntax-guided-localized-self-attention-by","title":"Syntax-guided Localized Self-attention by Constituency Syntactic Distance","date":"2022-10-21","arxiv_id":"2210.11759","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 2 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; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/syntax-guided-localized-self-attention-by#ran","syntology_url":"https://syntology.ai/paper/2210.11759","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11759"}},"official":{"repos":["lumia-group/distance_transformer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/turning-fixed-to-adaptive-integrating-post","slug":"turning-fixed-to-adaptive-integrating-post","title":"Turning Fixed to Adaptive: Integrating Post-Evaluation into Simultaneous Machine Translation","date":"2022-10-21","arxiv_id":"2210.11900","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/turning-fixed-to-adaptive-integrating-post#ran","syntology_url":"https://syntology.ai/paper/2210.11900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11900"}},"official":{"repos":["ictnlp/ped-simt"],"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/multi-granularity-optimization-for-non","slug":"multi-granularity-optimization-for-non","title":"Multi-Granularity Optimization for Non-Autoregressive Translation","date":"2022-10-20","arxiv_id":"2210.11017","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multi-granularity-optimization-for-non#ran","syntology_url":"https://syntology.ai/paper/2210.11017","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11017"}},"official":{"repos":["yafuly/mgmo-nat"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/the-university-of-edinburgh-s-submission-to","slug":"the-university-of-edinburgh-s-submission-to","title":"The University of Edinburgh's Submission to the WMT22 Code-Mixing Shared Task (MixMT)","date":"2022-10-20","arxiv_id":"2210.11309","repositories_listed":1,"syntology":null},{"url":"/paper/wait-info-policy-balancing-source-and-target","slug":"wait-info-policy-balancing-source-and-target","title":"Wait-info Policy: Balancing Source and Target at Information Level for Simultaneous Machine Translation","date":"2022-10-20","arxiv_id":"2210.11220","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/wait-info-policy-balancing-source-and-target#ran","syntology_url":"https://syntology.ai/paper/2210.11220","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11220"}},"official":{"repos":["ictnlp/wait-info"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/alibaba-translate-china-s-submission-for-wmt","slug":"alibaba-translate-china-s-submission-for-wmt","title":"Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task","date":"2022-10-18","arxiv_id":"2210.09683","repositories_listed":1,"syntology":null},{"url":"/paper/discrete-cross-modal-alignment-enables-zero","slug":"discrete-cross-modal-alignment-enables-zero","title":"Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation","date":"2022-10-18","arxiv_id":"2210.09556","repositories_listed":1,"syntology":null},{"url":"/paper/synergy-with-translation-artifacts-for","slug":"synergy-with-translation-artifacts-for","title":"Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks","date":"2022-10-18","arxiv_id":"2210.09588","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/synergy-with-translation-artifacts-for#ran","syntology_url":"https://syntology.ai/paper/2210.09588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09588"}},"official":{"repos":["jongwooko/musc"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/tencent-s-multilingual-machine-translation","slug":"tencent-s-multilingual-machine-translation","title":"Tencent's Multilingual Machine Translation System for WMT22 Large-Scale African Languages","date":"2022-10-18","arxiv_id":"2210.09644","repositories_listed":1,"syntology":null},{"url":"/paper/tencent-ai-lab-shanghai-jiao-tong-university","slug":"tencent-ai-lab-shanghai-jiao-tong-university","title":"Tencent AI Lab - Shanghai Jiao Tong University Low-Resource Translation System for the WMT22 Translation Task","date":"2022-10-17","arxiv_id":"2210.08742","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-context-with-linear-attention-for-1","slug":"modeling-context-with-linear-attention-for-1","title":"Modeling Context With Linear Attention for Scalable Document-Level Translation","date":"2022-10-16","arxiv_id":"2210.08431","repositories_listed":1,"syntology":null},{"url":"/paper/generating-synthetic-speech-from-spokenvocab","slug":"generating-synthetic-speech-from-spokenvocab","title":"Generating Synthetic Speech from SpokenVocab for Speech Translation","date":"2022-10-15","arxiv_id":"2210.08174","repositories_listed":1,"syntology":null},{"url":"/paper/crop-zero-shot-cross-lingual-named-entity","slug":"crop-zero-shot-cross-lingual-named-entity","title":"CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation","date":"2022-10-13","arxiv_id":"2210.07022","repositories_listed":1,"syntology":null},{"url":"/paper/low-resource-neural-machine-translation-with","slug":"low-resource-neural-machine-translation-with","title":"Low-resource Neural Machine Translation with Cross-modal Alignment","date":"2022-10-13","arxiv_id":"2210.06716","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/low-resource-neural-machine-translation-with#ran","syntology_url":"https://syntology.ai/paper/2210.06716","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06716"}},"official":{"repos":["ictnlp/lnmt-ca"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/scaling-back-translation-with-domain-text","slug":"scaling-back-translation-with-domain-text","title":"Scaling Back-Translation with Domain Text Generation for Sign Language Gloss Translation","date":"2022-10-13","arxiv_id":"2210.07054","repositories_listed":1,"syntology":null},{"url":"/paper/non-autoregressive-machine-translation-with-2","slug":"non-autoregressive-machine-translation-with-2","title":"Integrating Translation Memories into Non-Autoregressive Machine Translation","date":"2022-10-12","arxiv_id":"2210.06020","repositories_listed":1,"syntology":null},{"url":"/paper/silveralign-mt-based-silver-data-algorithm","slug":"silveralign-mt-based-silver-data-algorithm","title":"SilverAlign: MT-Based Silver Data Algorithm For Evaluating Word Alignment","date":"2022-10-12","arxiv_id":"2210.06207","repositories_listed":1,"syntology":null},{"url":"/paper/enriching-biomedical-knowledge-for-low","slug":"enriching-biomedical-knowledge-for-low","title":"Enriching Biomedical Knowledge for Low-resource Language Through Large-Scale Translation","date":"2022-10-11","arxiv_id":"2210.05598","repositories_listed":1,"syntology":null},{"url":"/paper/isovec-controlling-the-relative-isomorphism","slug":"isovec-controlling-the-relative-isomorphism","title":"IsoVec: Controlling the Relative Isomorphism of Word Embedding Spaces","date":"2022-10-11","arxiv_id":"2210.05098","repositories_listed":1,"syntology":null},{"url":"/paper/machine-translation-between-spoken-languages","slug":"machine-translation-between-spoken-languages","title":"Machine Translation between Spoken Languages and Signed Languages Represented in SignWriting","date":"2022-10-11","arxiv_id":"2210.05404","repositories_listed":1,"syntology":null},{"url":"/paper/viterbi-decoding-of-directed-acyclic","slug":"viterbi-decoding-of-directed-acyclic","title":"Viterbi Decoding of Directed Acyclic Transformer for Non-Autoregressive Machine Translation","date":"2022-10-11","arxiv_id":"2210.05193","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/viterbi-decoding-of-directed-acyclic#ran","syntology_url":"https://syntology.ai/paper/2210.05193","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.05193"}},"official":{"repos":["thu-coai/da-transformer"],"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/automatic-evaluation-and-analysis-of-idioms","slug":"automatic-evaluation-and-analysis-of-idioms","title":"Automatic Evaluation and Analysis of Idioms in Neural Machine Translation","date":"2022-10-10","arxiv_id":"2210.04545","repositories_listed":1,"syntology":null},{"url":"/paper/distill-the-image-to-nowhere-inversion","slug":"distill-the-image-to-nowhere-inversion","title":"Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation","date":"2022-10-10","arxiv_id":"2210.04468","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":2,"n_ran_checked":0,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/distill-the-image-to-nowhere-inversion#ran","syntology_url":"https://syntology.ai/paper/2210.04468","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.04468"}},"official":{"repos":["pengr/ikd-mmt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/computational-choreography-using-human-motion","slug":"computational-choreography-using-human-motion","title":"KP-RNN: A Deep Learning Pipeline for Human Motion Prediction and Synthesis of Performance Art","date":"2022-10-09","arxiv_id":"2210.04366","repositories_listed":1,"syntology":null},{"url":"/paper/cross-align-modeling-deep-cross-lingual","slug":"cross-align-modeling-deep-cross-lingual","title":"Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment","date":"2022-10-09","arxiv_id":"2210.04141","repositories_listed":1,"syntology":null},{"url":"/paper/bird-eye-transformers-for-text-generation","slug":"bird-eye-transformers-for-text-generation","title":"Bird-Eye Transformers for Text Generation Models","date":"2022-10-08","arxiv_id":"2210.03985","repositories_listed":1,"syntology":null},{"url":"/paper/improving-end-to-end-text-image-translation","slug":"improving-end-to-end-text-image-translation","title":"Improving End-to-End Text Image Translation From the Auxiliary Text Translation Task","date":"2022-10-08","arxiv_id":"2210.03887","repositories_listed":1,"syntology":null},{"url":"/paper/nmtsloth-understanding-and-testing-efficiency","slug":"nmtsloth-understanding-and-testing-efficiency","title":"LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language Models","date":"2022-10-07","arxiv_id":"2210.03696","repositories_listed":1,"syntology":null},{"url":"/paper/joeys2t-minimalistic-speech-to-text-modeling","slug":"joeys2t-minimalistic-speech-to-text-modeling","title":"JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMT","date":"2022-10-05","arxiv_id":"2210.02545","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/joeys2t-minimalistic-speech-to-text-modeling#ran","syntology_url":"https://syntology.ai/paper/2210.02545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02545"}},"official":{"repos":["may-/joeys2t"],"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/byte-based-multilingual-nmt-for-endangered","slug":"byte-based-multilingual-nmt-for-endangered","title":"Byte-based Multilingual NMT for Endangered Languages","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/codonmt-modeling-cohesion-devices-for","slug":"codonmt-modeling-cohesion-devices-for","title":"CoDoNMT: Modeling Cohesion Devices for Document-Level Neural Machine Translation","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/frmt-a-benchmark-for-few-shot-region-aware","slug":"frmt-a-benchmark-for-few-shot-region-aware","title":"FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation","date":"2022-10-01","arxiv_id":"2210.00193","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-constrained-back-translation-for","slug":"iterative-constrained-back-translation-for","title":"Iterative Constrained Back-Translation for Unsupervised Domain Adaptation of Machine Translation","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/low-resource-neural-machine-translation-a-1","slug":"low-resource-neural-machine-translation-a-1","title":"Low-Resource Neural Machine Translation: A Case Study of Cantonese","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multi-level-community-awareness-graph-neural","slug":"multi-level-community-awareness-graph-neural","title":"Multi-level Community-awareness Graph Neural Networks for Neural Machine Translation","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/museclir-a-multiple-senses-and-cross-lingual","slug":"museclir-a-multiple-senses-and-cross-lingual","title":"MuSeCLIR: A Multiple Senses and Cross-lingual Information Retrieval Dataset","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/speeding-up-transformer-decoding-via-an","slug":"speeding-up-transformer-decoding-via-an","title":"Speeding up Transformer Decoding via an Attention Refinement Network","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-curious-case-of-logistic-regression-for","slug":"the-curious-case-of-logistic-regression-for","title":"The Curious Case of Logistic Regression for Italian Languages and Dialects Identification","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-robust-neural-machine-translation-1","slug":"towards-robust-neural-machine-translation-1","title":"Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-based-image-translation-using","slug":"diffusion-based-image-translation-using","title":"Diffusion-based Image Translation using Disentangled Style and Content Representation","date":"2022-09-30","arxiv_id":"2209.15264","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/diffusion-based-image-translation-using#ran","syntology_url":"https://syntology.ai/paper/2209.15264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.15264"}},"official":{"repos":["anon294384/diffuseit"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/direct-speech-translation-for-automatic","slug":"direct-speech-translation-for-automatic","title":"Direct Speech Translation for Automatic Subtitling","date":"2022-09-27","arxiv_id":"2209.13192","repositories_listed":1,"syntology":null},{"url":"/paper/learning-invariant-representations-for-2","slug":"learning-invariant-representations-for-2","title":"Learning Invariant Representations for Equivariant Neural Networks Using Orthogonal Moments","date":"2022-09-22","arxiv_id":"2209.10944","repositories_listed":1,"syntology":null},{"url":"/paper/midms-matching-interleaved-diffusion-models","slug":"midms-matching-interleaved-diffusion-models","title":"MIDMs: Matching Interleaved Diffusion Models for Exemplar-based Image Translation","date":"2022-09-22","arxiv_id":"2209.11047","repositories_listed":1,"syntology":null},{"url":"/paper/semantically-consistent-data-augmentation-for","slug":"semantically-consistent-data-augmentation-for","title":"Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language Model","date":"2022-09-22","arxiv_id":"2209.10875","repositories_listed":1,"syntology":null},{"url":"/paper/dodging-the-data-bottleneck-automatic","slug":"dodging-the-data-bottleneck-automatic","title":"Dodging the Data Bottleneck: Automatic Subtitling with Automatically Segmented ST Corpora","date":"2022-09-21","arxiv_id":"2209.10608","repositories_listed":1,"syntology":null},{"url":"/paper/vega-mt-the-jd-explore-academy-translation","slug":"vega-mt-the-jd-explore-academy-translation","title":"Vega-MT: The JD Explore Academy Translation System for WMT22","date":"2022-09-20","arxiv_id":"2209.09444","repositories_listed":1,"syntology":null},{"url":"/paper/vitag-online-wifi-fine-time-measurements","slug":"vitag-online-wifi-fine-time-measurements","title":"ViTag: Online WiFi Fine Time Measurements Aided Vision-Motion Identity Association in Multi-person Environments","date":"2022-09-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/t2v-ddpm-thermal-to-visible-face-translation","slug":"t2v-ddpm-thermal-to-visible-face-translation","title":"T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models","date":"2022-09-19","arxiv_id":"2209.08814","repositories_listed":1,"syntology":null},{"url":"/paper/the-first-neural-machine-translation-system","slug":"the-first-neural-machine-translation-system","title":"The first neural machine translation system for the Erzya language","date":"2022-09-19","arxiv_id":"2209.09368","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-round-trip-translation-for","slug":"rethinking-round-trip-translation-for","title":"Rethinking Round-Trip Translation for Machine Translation Evaluation","date":"2022-09-15","arxiv_id":"2209.07351","repositories_listed":1,"syntology":null},{"url":"/paper/expressive-reasoning-graph-store-a-unified","slug":"expressive-reasoning-graph-store-a-unified","title":"Expressive Reasoning Graph Store: A Unified Framework for Managing RDF and Property Graph Databases","date":"2022-09-13","arxiv_id":"2209.05828","repositories_listed":1,"syntology":null},{"url":"/paper/rethink-about-the-word-level-quality","slug":"rethink-about-the-word-level-quality","title":"Rethink about the Word-level Quality Estimation for Machine Translation from Human Judgement","date":"2022-09-13","arxiv_id":"2209.05695","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-neural-scaling-laws-in-language","slug":"revisiting-neural-scaling-laws-in-language","title":"Revisiting Neural Scaling Laws in Language and Vision","date":"2022-09-13","arxiv_id":"2209.06640","repositories_listed":1,"syntology":null},{"url":"/paper/time-of-day-neural-style-transfer-for","slug":"time-of-day-neural-style-transfer-for","title":"Time-of-Day Neural Style Transfer for Architectural Photographs","date":"2022-09-13","arxiv_id":"2209.05800","repositories_listed":1,"syntology":null},{"url":"/paper/towards-multi-lingual-visual-question","slug":"towards-multi-lingual-visual-question","title":"MaXM: Towards Multilingual Visual Question Answering","date":"2022-09-12","arxiv_id":"2209.05401","repositories_listed":1,"syntology":null},{"url":"/paper/adapting-to-non-centered-languages-for-zero","slug":"adapting-to-non-centered-languages-for-zero","title":"Adapting to Non-Centered Languages for Zero-shot Multilingual Translation","date":"2022-09-09","arxiv_id":"2209.04138","repositories_listed":1,"syntology":null},{"url":"/paper/maxmatch-dropout-subword-regularization-for","slug":"maxmatch-dropout-subword-regularization-for","title":"MaxMatch-Dropout: Subword Regularization for WordPiece","date":"2022-09-09","arxiv_id":"2209.04126","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-machine-translation-for-cross","slug":"leveraging-machine-translation-for-cross","title":"Leveraging machine translation for cross-lingual fine-grained cyberbullying classification amongst pre-adolescents","date":"2022-09-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/on-the-complementarity-between-pre-training-1","slug":"on-the-complementarity-between-pre-training-1","title":"On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation","date":"2022-09-07","arxiv_id":"2209.03316","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":11,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 1 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/on-the-complementarity-between-pre-training-1#ran","syntology_url":"https://syntology.ai/paper/2209.03316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.03316"}},"official":{"repos":["zanchangtong/ptvsri"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}}],"record_sha256":"b371e4ff59c645d0c6cd3805450dca5ff6532c497d16ab4865db8250a88c1e02","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}