{"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":"/method/dropout/papers/234","list_of":"/method/dropout","method":"Dropout","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":234,"pages_in_order":275,"rows_per_page":100,"rows":[23301,23400],"of":27472,"counts":{"archive_papers_tagged":27472,"with_a_code_link":12129,"where_syntology_ran_a_sample":3620,"not_listed_spam_title":0,"listed":27472,"listed_where_code_ran":3620,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3044,"every_run_a_failure_of_syntologys_instrument":576,"listed_with_a_run_with_no_instrument_failure":3044,"listed_every_run_a_failure_of_syntologys_instrument":576,"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":"/method/dropout","prev":"/method/dropout/papers/233","next":"/method/dropout/papers/235","papers":[{"paper":"/paper/a-co-interactive-transformer-for-joint-slot","slug":"a-co-interactive-transformer-for-joint-slot","title":"A Co-Interactive Transformer for Joint Slot Filling and Intent Detection","date":"2020-10-08","arxiv_id":"2010.03880","n_code_links":1,"syntology":null},{"paper":"/paper/automatic-generation-of-reviews-of-scientific","slug":"automatic-generation-of-reviews-of-scientific","title":"Automatic generation of reviews of scientific papers","date":"2020-10-08","arxiv_id":"2010.04147","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-meets-projective-clustering-1","title":"Deep Learning Meets Projective Clustering","date":"2020-10-08","arxiv_id":"2010.04290","n_code_links":0,"syntology":null},{"paper":"/paper/deformable-detr-deformable-transformers-for-1","slug":"deformable-detr-deformable-transformers-for-1","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","date":"2020-10-08","arxiv_id":"2010.04159","n_code_links":20,"syntology":{"ran":37,"of":55,"n_ran_checked":24,"n_instrument":13,"unverified":18,"pointer_only":21,"phrase":"37 ran (of which 9 constructed an object rather than computing a result; 24 with no instrument failure: 1 honoured, 3 violated, 20 with no contract checked; 13 where Syntology's instrument failed) · 18 unverified","official":{"repos":["fundamentalvision/Deformable-DETR"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/discriminatively-tuned-generative-classifiers","slug":"discriminatively-tuned-generative-classifiers","title":"Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference","date":"2020-10-08","arxiv_id":"2010.03760","n_code_links":1,"syntology":null},{"paper":"/paper/energy-based-out-of-distribution-detection-1","slug":"energy-based-out-of-distribution-detection-1","title":"Energy-based Out-of-distribution Detection","date":"2020-10-08","arxiv_id":"2010.03759","n_code_links":6,"syntology":{"ran":3,"of":6,"n_ran_checked":1,"n_instrument":2,"unverified":3,"pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 3 unverified","official":{"repos":["wetliu/energy_ood"],"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":["listed","official"]}}},{"paper":null,"slug":"improving-attention-mechanism-with-query","title":"Improving Attention Mechanism with Query-Value Interaction","date":"2020-10-08","arxiv_id":"2010.03766","n_code_links":0,"syntology":null},{"paper":"/paper/infusing-disease-knowledge-into-bert-for","slug":"infusing-disease-knowledge-into-bert-for","title":"Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition","date":"2020-10-08","arxiv_id":"2010.03746","n_code_links":1,"syntology":null},{"paper":"/paper/injecting-word-information-with-multi-level","slug":"injecting-word-information-with-multi-level","title":"Injecting Word Information with Multi-Level Word Adapter for Chinese Spoken Language Understanding","date":"2020-10-08","arxiv_id":"2010.03903","n_code_links":1,"syntology":null},{"paper":"/paper/interlocking-backpropagation-improving","slug":"interlocking-backpropagation-improving","title":"Interlocking Backpropagation: Improving depthwise model-parallelism","date":"2020-10-08","arxiv_id":"2010.04116","n_code_links":1,"syntology":null},{"paper":null,"slug":"leveraging-unpaired-text-data-for-training","title":"Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems","date":"2020-10-08","arxiv_id":"2010.04284","n_code_links":0,"syntology":null},{"paper":null,"slug":"masked-elmo-an-evolution-of-elmo-towards","title":"Masked ELMo: An evolution of ELMo towards fully contextual RNN language models","date":"2020-10-08","arxiv_id":"2010.04302","n_code_links":0,"syntology":null},{"paper":"/paper/non-attentive-tacotron-robust-and-1","slug":"non-attentive-tacotron-robust-and-1","title":"Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling","date":"2020-10-08","arxiv_id":"2010.04301","n_code_links":6,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"6 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; 0 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/parade-a-new-dataset-for-paraphrase","slug":"parade-a-new-dataset-for-paraphrase","title":"PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge","date":"2020-10-08","arxiv_id":"2010.03725","n_code_links":1,"syntology":null},{"paper":null,"slug":"prediction-intervals-for-deep-neural-networks","title":"Prediction intervals for Deep Neural Networks","date":"2020-10-08","arxiv_id":"2010.04044","n_code_links":0,"syntology":null},{"paper":null,"slug":"r-mnasnet-reduced-mnasnet-for-computer-vision","title":"R-MnasNet: Reduced MnasNet for Computer Vision","date":"2020-10-08","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/shallow-to-deep-training-for-neural-machine","slug":"shallow-to-deep-training-for-neural-machine","title":"Shallow-to-Deep Training for Neural Machine Translation","date":"2020-10-08","arxiv_id":"2010.03737","n_code_links":1,"syntology":null},{"paper":"/paper/textsettr-label-free-text-style-extraction-1","slug":"textsettr-label-free-text-style-extraction-1","title":"TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling","date":"2020-10-08","arxiv_id":"2010.03802","n_code_links":1,"syntology":null},{"paper":"/paper/visualnews-a-large-multi-source-news-image","slug":"visualnews-a-large-multi-source-news-image","title":"Visual News: Benchmark and Challenges in News Image Captioning","date":"2020-10-08","arxiv_id":"2010.03743","n_code_links":1,"syntology":null},{"paper":null,"slug":"combining-deep-learning-and-string-kernels","title":"Combining Deep Learning and String Kernels for the Localization of Swiss German Tweets","date":"2020-10-07","arxiv_id":"2010.03614","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-fine-grained-cross-lingual-semantic","slug":"detecting-fine-grained-cross-lingual-semantic","title":"Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank","date":"2020-10-07","arxiv_id":"2010.03662","n_code_links":1,"syntology":null},{"paper":null,"slug":"dipair-fast-and-accurate-distillation-for","title":"DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling","date":"2020-10-07","arxiv_id":"2010.03099","n_code_links":0,"syntology":null},{"paper":null,"slug":"don-t-trigger-me-a-triggerless-backdoor-1","title":"Don't Trigger Me! A Triggerless Backdoor Attack Against Deep Neural Networks","date":"2020-10-07","arxiv_id":"2010.03282","n_code_links":0,"syntology":null},{"paper":null,"slug":"elmo-and-bert-in-semantic-change-detection","title":"ELMo and BERT in semantic change detection for Russian","date":"2020-10-07","arxiv_id":"2010.03481","n_code_links":0,"syntology":null},{"paper":"/paper/low-resource-domain-adaptation-for","slug":"low-resource-domain-adaptation-for","title":"Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing","date":"2020-10-07","arxiv_id":"2010.03546","n_code_links":1,"syntology":null},{"paper":"/paper/optimizing-transformers-with-approximate-1","slug":"optimizing-transformers-with-approximate-1","title":"AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP Models","date":"2020-10-07","arxiv_id":"2010.03688","n_code_links":1,"syntology":null},{"paper":"/paper/super-human-performance-in-online-low-latency","slug":"super-human-performance-in-online-low-latency","title":"Super-Human Performance in Online Low-latency Recognition of Conversational Speech","date":"2020-10-07","arxiv_id":"2010.03449","n_code_links":1,"syntology":null},{"paper":"/paper/synthesising-clinically-realistic-chest-x","slug":"synthesising-clinically-realistic-chest-x","title":"Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs","date":"2020-10-07","arxiv_id":"2010.03975","n_code_links":1,"syntology":null},{"paper":"/paper/transformer-gcrf-recovering-chinese-dropped","slug":"transformer-gcrf-recovering-chinese-dropped","title":"Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields","date":"2020-10-07","arxiv_id":"2010.03224","n_code_links":1,"syntology":null},{"paper":"/paper/where-are-the-facts-searching-for-fact","slug":"where-are-the-facts-searching-for-fact","title":"Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News","date":"2020-10-07","arxiv_id":"2010.03159","n_code_links":2,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"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","official":{"repos":["nguyenvo09/EMNLP2020"],"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"]}}},{"paper":"/paper/why-do-you-think-that-exploring-faithful","slug":"why-do-you-think-that-exploring-faithful","title":"Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision","date":"2020-10-07","arxiv_id":"2010.03384","n_code_links":1,"syntology":null},{"paper":null,"slug":"adversarial-grammatical-error-correction","title":"Adversarial Grammatical Error Correction","date":"2020-10-06","arxiv_id":"2010.02407","n_code_links":0,"syntology":null},{"paper":"/paper/analyzing-individual-neurons-in-pre-trained","slug":"analyzing-individual-neurons-in-pre-trained","title":"Analyzing Individual Neurons in Pre-trained Language Models","date":"2020-10-06","arxiv_id":"2010.02695","n_code_links":1,"syntology":null},{"paper":"/paper/bert-knows-punta-cana-is-not-just-beautiful","slug":"bert-knows-punta-cana-is-not-just-beautiful","title":"BERT Knows Punta Cana is not just beautiful, it's gorgeous: Ranking Scalar Adjectives with Contextualised Representations","date":"2020-10-06","arxiv_id":"2010.02686","n_code_links":1,"syntology":null},{"paper":null,"slug":"beyond-cls-through-ranking-by-generation","title":"Beyond [CLS] through Ranking by Generation","date":"2020-10-06","arxiv_id":"2010.03073","n_code_links":0,"syntology":null},{"paper":"/paper/converting-the-point-of-view-of-messages","slug":"converting-the-point-of-view-of-messages","title":"Converting the Point of View of Messages Spoken to Virtual Assistants","date":"2020-10-06","arxiv_id":"2010.02600","n_code_links":2,"syntology":null},{"paper":"/paper/cross-lingual-text-classification-with","slug":"cross-lingual-text-classification-with","title":"Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher","date":"2020-10-06","arxiv_id":"2010.02562","n_code_links":1,"syntology":null},{"paper":"/paper/do-explicit-alignments-robustly-improve","slug":"do-explicit-alignments-robustly-improve","title":"Do Explicit Alignments Robustly Improve Multilingual Encoders?","date":"2020-10-06","arxiv_id":"2010.02537","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-inference-for-neural-machine","title":"Efficient Inference For Neural Machine Translation","date":"2020-10-06","arxiv_id":"2010.02416","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-bert-s-sensitivity-to-lexical-cues","slug":"exploring-bert-s-sensitivity-to-lexical-cues","title":"Exploring BERT's Sensitivity to Lexical Cues using Tests from Semantic Priming","date":"2020-10-06","arxiv_id":"2010.03010","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":0,"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","official":{"repos":["kanishkamisra/emnlp-bert-priming"],"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"]}}},{"paper":"/paper/improving-efficient-neural-ranking-models","slug":"improving-efficient-neural-ranking-models","title":"Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation","date":"2020-10-06","arxiv_id":"2010.02666","n_code_links":1,"syntology":null},{"paper":null,"slug":"incorporating-behavioral-hypotheses-for-query","title":"Incorporating Behavioral Hypotheses for Query Generation","date":"2020-10-06","arxiv_id":"2010.02667","n_code_links":0,"syntology":null},{"paper":"/paper/intrinsic-probing-through-dimension-selection","slug":"intrinsic-probing-through-dimension-selection","title":"Intrinsic Probing through Dimension Selection","date":"2020-10-06","arxiv_id":"2010.02812","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":0,"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","official":{"repos":["rycolab/intrinsic-probing"],"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"]}}},{"paper":"/paper/investigating-african-american-vernacular","slug":"investigating-african-american-vernacular","title":"Investigating African-American Vernacular English in Transformer-Based Text Generation","date":"2020-10-06","arxiv_id":"2010.02510","n_code_links":1,"syntology":null},{"paper":null,"slug":"legal-bert-the-muppets-straight-out-of-law","title":"LEGAL-BERT: The Muppets straight out of Law School","date":"2020-10-06","arxiv_id":"2010.02559","n_code_links":0,"syntology":null},{"paper":"/paper/neural-mask-generator-learning-to-generate","slug":"neural-mask-generator-learning-to-generate","title":"Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation","date":"2020-10-06","arxiv_id":"2010.02705","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-interplay-between-fine-tuning-and","title":"On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers","date":"2020-10-06","arxiv_id":"2010.02616","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-sub-layer-functionalities-of","title":"On the Sub-Layer Functionalities of Transformer Decoder","date":"2020-10-06","arxiv_id":"2010.02648","n_code_links":0,"syntology":null},{"paper":null,"slug":"parallax-motion-effect-generation-through","title":"Parallax Motion Effect Generation Through Instance Segmentation And Depth Estimation","date":"2020-10-06","arxiv_id":"2010.02680","n_code_links":0,"syntology":null},{"paper":"/paper/poison-attacks-against-text-datasets-with","slug":"poison-attacks-against-text-datasets-with","title":"Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder","date":"2020-10-06","arxiv_id":"2010.02684","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["alvinchangw/CARA_EMNLP2020"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"resource-enhanced-neural-model-for-event","title":"Resource-Enhanced Neural Model for Event Argument Extraction","date":"2020-10-06","arxiv_id":"2010.03022","n_code_links":0,"syntology":null},{"paper":"/paper/scene-graph-modification-based-on-natural","slug":"scene-graph-modification-based-on-natural","title":"Scene Graph Modification Based on Natural Language Commands","date":"2020-10-06","arxiv_id":"2010.02591","n_code_links":1,"syntology":null},{"paper":"/paper/the-multilingual-amazon-reviews-corpus","slug":"the-multilingual-amazon-reviews-corpus","title":"The Multilingual Amazon Reviews Corpus","date":"2020-10-06","arxiv_id":"2010.02573","n_code_links":1,"syntology":null},{"paper":null,"slug":"vector-vector-matrix-architecture-a-novel","title":"Vector-Vector-Matrix Architecture: A Novel Hardware-Aware Framework for Low-Latency Inference in NLP Applications","date":"2020-10-06","arxiv_id":"2010.08412","n_code_links":0,"syntology":null},{"paper":"/paper/d3net-densely-connected-multidilated-densenet","slug":"d3net-densely-connected-multidilated-densenet","title":"D3Net: Densely connected multidilated DenseNet for music source separation","date":"2020-10-05","arxiv_id":"2010.01733","n_code_links":1,"syntology":null},{"paper":"/paper/dct-snn-using-dct-to-distribute-spatial-1","slug":"dct-snn-using-dct-to-distribute-spatial-1","title":"DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks","date":"2020-10-05","arxiv_id":"2010.01795","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"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","official":{"repos":["SayeedChowdhury/dct-snn"],"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"]}}},{"paper":null,"slug":"gauravarora-hasoc-dravidian-codemix-fire2020","title":"Gauravarora@HASOC-Dravidian-CodeMix-FIRE2020: Pre-training ULMFiT on Synthetically Generated Code-Mixed Data for Hate Speech Detection","date":"2020-10-05","arxiv_id":"2010.02094","n_code_links":0,"syntology":null},{"paper":"/paper/genaug-data-augmentation-for-finetuning-text","slug":"genaug-data-augmentation-for-finetuning-text","title":"GenAug: Data Augmentation for Finetuning Text Generators","date":"2020-10-05","arxiv_id":"2010.01794","n_code_links":2,"syntology":null},{"paper":null,"slug":"how-effective-is-task-agnostic-data","title":"How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?","date":"2020-10-05","arxiv_id":"2010.01764","n_code_links":0,"syntology":null},{"paper":"/paper/improving-amr-parsing-with-sequence-to","slug":"improving-amr-parsing-with-sequence-to","title":"Improving AMR Parsing with Sequence-to-Sequence Pre-training","date":"2020-10-05","arxiv_id":"2010.01771","n_code_links":1,"syntology":null},{"paper":"/paper/infobert-improving-robustness-of-language-1","slug":"infobert-improving-robustness-of-language-1","title":"InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective","date":"2020-10-05","arxiv_id":"2010.02329","n_code_links":2,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["AI-secure/InfoBERT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"linguistic-profiling-of-a-neural-language","title":"Linguistic Profiling of a Neural Language Model","date":"2020-10-05","arxiv_id":"2010.01869","n_code_links":0,"syntology":null},{"paper":null,"slug":"mixup-transfomer-dynamic-data-augmentation","title":"Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks","date":"2020-10-05","arxiv_id":"2010.02394","n_code_links":0,"syntology":null},{"paper":null,"slug":"pair-planning-and-iterative-refinement-in-pre","title":"PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation","date":"2020-10-05","arxiv_id":"2010.02301","n_code_links":0,"syntology":null},{"paper":"/paper/pareto-probing-trading-off-accuracy-for","slug":"pareto-probing-trading-off-accuracy-for","title":"Pareto Probing: Trading Off Accuracy for Complexity","date":"2020-10-05","arxiv_id":"2010.02180","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["rycolab/pareto-probing"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/pmi-masking-principled-masking-of-correlated-1","slug":"pmi-masking-principled-masking-of-correlated-1","title":"PMI-Masking: Principled masking of correlated spans","date":"2020-10-05","arxiv_id":"2010.01825","n_code_links":1,"syntology":null},{"paper":"/paper/pruning-redundant-mappings-in-transformer","slug":"pruning-redundant-mappings-in-transformer","title":"Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior","date":"2020-10-05","arxiv_id":"2010.01791","n_code_links":1,"syntology":null},{"paper":null,"slug":"pum-at-semeval-2020-task-12-aggregation-of","title":"PUM at SemEval-2020 Task 12: Aggregation of Transformer-based models' features for offensive language recognition","date":"2020-10-05","arxiv_id":"2010.01897","n_code_links":0,"syntology":null},{"paper":"/paper/self-training-improves-pre-training-for","slug":"self-training-improves-pre-training-for","title":"Self-training Improves Pre-training for Natural Language Understanding","date":"2020-10-05","arxiv_id":"2010.02194","n_code_links":1,"syntology":null},{"paper":"/paper/transformer-based-neural-text-generation-with","slug":"transformer-based-neural-text-generation-with","title":"Transformer-Based Neural Text Generation with Syntactic Guidance","date":"2020-10-05","arxiv_id":"2010.01737","n_code_links":1,"syntology":null},{"paper":"/paper/unsupervised-reference-free-summary-quality","slug":"unsupervised-reference-free-summary-quality","title":"Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning","date":"2020-10-05","arxiv_id":"2010.01781","n_code_links":1,"syntology":null},{"paper":"/paper/using-bayesian-deep-learning-approaches-for","slug":"using-bayesian-deep-learning-approaches-for","title":"Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models","date":"2020-10-05","arxiv_id":"2010.03029","n_code_links":1,"syntology":null},{"paper":"/paper/x-srl-a-parallel-cross-lingual-semantic-role","slug":"x-srl-a-parallel-cross-lingual-semantic-role","title":"X-SRL: A Parallel Cross-Lingual Semantic Role Labeling Dataset","date":"2020-10-05","arxiv_id":"2010.01998","n_code_links":1,"syntology":null},{"paper":"/paper/an-empirical-study-on-large-scale-multi-label","slug":"an-empirical-study-on-large-scale-multi-label","title":"An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels","date":"2020-10-04","arxiv_id":"2010.01653","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":6,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"7 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; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["iliaschalkidis/lmtc-eurlex57k"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/inquisitive-question-generation-for-high","slug":"inquisitive-question-generation-for-high","title":"Inquisitive Question Generation for High Level Text Comprehension","date":"2020-10-04","arxiv_id":"2010.01657","n_code_links":1,"syntology":null},{"paper":"/paper/on-losses-for-modern-language-models","slug":"on-losses-for-modern-language-models","title":"On Losses for Modern Language Models","date":"2020-10-04","arxiv_id":"2010.01694","n_code_links":1,"syntology":null},{"paper":"/paper/tell-me-how-to-ask-again-question-data","slug":"tell-me-how-to-ask-again-question-data","title":"Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space","date":"2020-10-04","arxiv_id":"2010.01475","n_code_links":1,"syntology":null},{"paper":"/paper/differentially-private-representation-for-nlp","slug":"differentially-private-representation-for-nlp","title":"Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness","date":"2020-10-03","arxiv_id":"2010.01285","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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","official":{"repos":["xlhex/dpnlp"],"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"]}}},{"paper":null,"slug":"end-to-end-training-of-cnn-ensembles-for","title":"End-to-End Training of CNN Ensembles for Person Re-Identification","date":"2020-10-03","arxiv_id":"2010.01342","n_code_links":0,"syntology":null},{"paper":"/paper/mining-knowledge-for-natural-language","slug":"mining-knowledge-for-natural-language","title":"Mining Knowledge for Natural Language Inference from Wikipedia Categories","date":"2020-10-03","arxiv_id":"2010.01239","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["ZeweiChu/WikiNLI"],"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"]}}},{"paper":"/paper/nonconvex-regularization-for-network-slimming","slug":"nonconvex-regularization-for-network-slimming","title":"Improving Network Slimming with Nonconvex Regularization","date":"2020-10-03","arxiv_id":"2010.01242","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["kbui1993/NonconvexNetworkSlimming"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"personality-trait-detection-using-bagged-svm","title":"Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles","date":"2020-10-03","arxiv_id":"2010.01309","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-chemical-1d-knowledge-using","title":"Beyond Chemical 1D knowledge using Transformers","date":"2020-10-02","arxiv_id":"2010.01027","n_code_links":0,"syntology":null},{"paper":"/paper/cost-effective-selection-of-pretraining-data","slug":"cost-effective-selection-of-pretraining-data","title":"Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media","date":"2020-10-02","arxiv_id":"2010.01150","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-transfer-approaches-to-improve-seq-to","title":"Data Transfer Approaches to Improve Seq-to-Seq Retrosynthesis","date":"2020-10-02","arxiv_id":"2010.00792","n_code_links":0,"syntology":null},{"paper":null,"slug":"long-tail-zero-and-few-shot-learning-via","title":"Data-Efficient Pretraining via Contrastive Self-Supervision","date":"2020-10-02","arxiv_id":"2010.01061","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":9,"syntology":{"ran":3,"of":10,"n_ran_checked":3,"n_instrument":0,"unverified":7,"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","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"]}}},{"paper":"/paper/multicqa-zero-shot-transfer-of-self","slug":"multicqa-zero-shot-transfer-of-self","title":"MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale","date":"2020-10-02","arxiv_id":"2010.00980","n_code_links":1,"syntology":null},{"paper":"/paper/stil-simultaneous-slot-filling-translation","slug":"stil-simultaneous-slot-filling-translation","title":"STIL -- Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++","date":"2020-10-02","arxiv_id":"2010.00760","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-compare-aggregate-transformer-for","title":"A Compare Aggregate Transformer for Understanding Document-grounded Dialogue","date":"2020-10-01","arxiv_id":"2010.00190","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-the-text-analysis-of-privacy","title":"Beyond The Text: Analysis of Privacy Statements through Syntactic and Semantic Role Labeling","date":"2020-10-01","arxiv_id":"2010.00678","n_code_links":0,"syntology":null},{"paper":"/paper/colake-contextualized-language-and-knowledge","slug":"colake-contextualized-language-and-knowledge","title":"CoLAKE: Contextualized Language and Knowledge Embedding","date":"2020-10-01","arxiv_id":"2010.00309","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"6 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; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["txsun1997/CoLAKE"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"detecting-white-supremacist-hate-speech-using","title":"Detecting White Supremacist Hate Speech using Domain Specific Word Embedding with Deep Learning and BERT","date":"2020-10-01","arxiv_id":"2010.00357","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-multilingual-bert-for-estonian","title":"Evaluating Multilingual BERT for Estonian","date":"2020-10-01","arxiv_id":"2010.00454","n_code_links":0,"syntology":null},{"paper":null,"slug":"examining-the-rhetorical-capacities-of-neural","title":"Examining the rhetorical capacities of neural language models","date":"2020-10-01","arxiv_id":"2010.00153","n_code_links":0,"syntology":null},{"paper":null,"slug":"lane-change-for-system-driven-vehicles-using","title":"Lane Change For System-Driven Vehicles Using Dynamic Information","date":"2020-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/phonemer-at-wnut-2020-task-2-sequence","slug":"phonemer-at-wnut-2020-task-2-sequence","title":"Phonemer at WNUT-2020 Task 2: Sequence Classification Using COVID Twitter BERT and Bagging Ensemble Technique based on Plurality Voting","date":"2020-10-01","arxiv_id":"2010.00294","n_code_links":1,"syntology":null},{"paper":"/paper/refvos-a-closer-look-at-referring-expressions","slug":"refvos-a-closer-look-at-referring-expressions","title":"RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation","date":"2020-10-01","arxiv_id":"2010.00263","n_code_links":2,"syntology":null},{"paper":null,"slug":"rrf102-meeting-the-trec-covid-challenge-with","title":"RRF102: Meeting the TREC-COVID Challenge with a 100+ Runs Ensemble","date":"2020-10-01","arxiv_id":"2010.00200","n_code_links":0,"syntology":null},{"paper":"/paper/transformers-state-of-the-art-natural-1","slug":"transformers-state-of-the-art-natural-1","title":"Transformers: State-of-the-Art Natural Language Processing","date":"2020-10-01","arxiv_id":null,"n_code_links":3,"syntology":null}],"record_sha256":"5d03355096f9c740093b18e29afeb510805e332f929d78ec4e2cf9bfed05152e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}