{"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/wordpiece/papers/40","list_of":"/method/wordpiece","method":"WordPiece","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":40,"pages_in_order":71,"rows_per_page":100,"rows":[3901,4000],"of":7063,"counts":{"archive_papers_tagged":7063,"with_a_code_link":2910,"where_syntology_ran_a_sample":650,"not_listed_spam_title":0,"listed":7063,"listed_where_code_ran":650,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":529,"every_run_a_failure_of_syntologys_instrument":121,"listed_with_a_run_with_no_instrument_failure":529,"listed_every_run_a_failure_of_syntologys_instrument":121,"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/wordpiece","prev":"/method/wordpiece/papers/39","next":"/method/wordpiece/papers/41","papers":[{"paper":null,"slug":"can-machine-learning-tools-support-the","title":"Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges","date":"2021-12-17","arxiv_id":"2112.09391","n_code_links":0,"syntology":null},{"paper":null,"slug":"challenging-america-modeling-language-in","title":"Challenging America: Modeling language in longer time scales","date":"2021-12-17","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/explain-edit-and-understand-rethinking-user","slug":"explain-edit-and-understand-rethinking-user","title":"Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations","date":"2021-12-17","arxiv_id":"2112.09669","n_code_links":1,"syntology":null},{"paper":null,"slug":"joint-chinese-word-segmentation-and-part-of-2","title":"Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-stage Span Labeling","date":"2021-12-17","arxiv_id":"2112.09488","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-win-lottery-tickets-in-bert","title":"Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training","date":"2021-12-17","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"rank4class-a-ranking-formulation-for","title":"Rank4Class: A Ranking Formulation for Multiclass Classification","date":"2021-12-17","arxiv_id":"2112.09727","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-faithful-personalized-response","title":"Towards Faithful Personalized Response Selection in Retrieval Based Dialog Systems","date":"2021-12-17","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"an-empirical-study-on-transfer-learning-for","title":"An Empirical Study on Transfer Learning for Privilege Review","date":"2021-12-16","arxiv_id":"2112.08606","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"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","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"]}}},{"paper":null,"slug":"does-pre-training-induce-systematic-inference","title":"Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge","date":"2021-12-16","arxiv_id":"2112.08583","n_code_links":0,"syntology":null},{"paper":null,"slug":"3d-question-answering","title":"3D Question Answering","date":"2021-12-15","arxiv_id":"2112.08359","n_code_links":0,"syntology":null},{"paper":null,"slug":"applying-softtriple-loss-for-supervised","title":"Applying SoftTriple Loss for Supervised Language Model Fine Tuning","date":"2021-12-15","arxiv_id":"2112.08462","n_code_links":0,"syntology":null},{"paper":null,"slug":"fine-tuning-large-neural-language-models-for","title":"Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing","date":"2021-12-15","arxiv_id":"2112.07869","n_code_links":0,"syntology":null},{"paper":"/paper/one-size-does-not-fit-all-investigating","slug":"one-size-does-not-fit-all-investigating","title":"One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks","date":"2021-12-15","arxiv_id":"2112.08159","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["trusthlt/dp-across-nlp-tasks"],"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"]}}},{"paper":null,"slug":"one-system-to-rule-them-all-a-universal","title":"One System to Rule them All: a Universal Intent Recognition System for Customer Service Chatbots","date":"2021-12-15","arxiv_id":"2112.08261","n_code_links":0,"syntology":null},{"paper":null,"slug":"tracing-text-provenance-via-context-aware","title":"Tracing Text Provenance via Context-Aware Lexical Substitution","date":"2021-12-15","arxiv_id":"2112.07873","n_code_links":0,"syntology":null},{"paper":null,"slug":"ace-bert-adversarial-cross-modal-enhanced","title":"ACE-BERT: Adversarial Cross-modal Enhanced BERT for E-commerce Retrieval","date":"2021-12-14","arxiv_id":"2112.07209","n_code_links":0,"syntology":null},{"paper":null,"slug":"building-on-huang-et-al-glossbert-for-word","title":"Building on Huang et al. GlossBERT for Word Sense Disambiguation","date":"2021-12-14","arxiv_id":"2112.07089","n_code_links":0,"syntology":null},{"paper":null,"slug":"classifying-emails-into-human-vs-machine","title":"Classifying Emails into Human vs Machine Category","date":"2021-12-14","arxiv_id":"2112.07742","n_code_links":0,"syntology":null},{"paper":null,"slug":"coco-bert-improving-video-language-pre","title":"CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising","date":"2021-12-14","arxiv_id":"2112.07515","n_code_links":0,"syntology":null},{"paper":null,"slug":"epigenomic-language-models-powered-by","title":"Epigenomic language models powered by Cerebras","date":"2021-12-14","arxiv_id":"2112.07571","n_code_links":0,"syntology":null},{"paper":"/paper/from-dense-to-sparse-contrastive-pruning-for","slug":"from-dense-to-sparse-contrastive-pruning-for","title":"From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression","date":"2021-12-14","arxiv_id":"2112.07198","n_code_links":2,"syntology":null},{"paper":"/paper/measuring-fairness-with-biased-rulers-a","slug":"measuring-fairness-with-biased-rulers-a","title":"Measuring Fairness with Biased Rulers: A Survey on Quantifying Biases in Pretrained Language Models","date":"2021-12-14","arxiv_id":"2112.07447","n_code_links":1,"syntology":null},{"paper":"/paper/text-classification-models-for-form-entity","slug":"text-classification-models-for-form-entity","title":"Text Classification Models for Form Entity Linking","date":"2021-12-14","arxiv_id":"2112.07443","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-a-unified-foundation-model-jointly","title":"Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text","date":"2021-12-14","arxiv_id":"2112.07074","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-study-on-token-pruning-for-colbert","title":"A Study on Token Pruning for ColBERT","date":"2021-12-13","arxiv_id":"2112.06540","n_code_links":0,"syntology":null},{"paper":null,"slug":"context-vs-target-word-quantifying-biases-in","title":"Measuring Context-Word Biases in Lexical Semantic Datasets","date":"2021-12-13","arxiv_id":"2112.06733","n_code_links":0,"syntology":null},{"paper":null,"slug":"do-data-based-curricula-work","title":"Do Data-based Curricula Work?","date":"2021-12-13","arxiv_id":"2112.06510","n_code_links":0,"syntology":null},{"paper":"/paper/keyphrase-generation-beyond-the-boundaries-of","slug":"keyphrase-generation-beyond-the-boundaries-of","title":"Keyphrase Generation Beyond the Boundaries of Title and Abstract","date":"2021-12-13","arxiv_id":"2112.06776","n_code_links":1,"syntology":null},{"paper":null,"slug":"roof-bert-divide-understanding-labour-and","title":"Roof-Transformer: Divided and Joined Understanding with Knowledge Enhancement","date":"2021-12-13","arxiv_id":"2112.06736","n_code_links":0,"syntology":null},{"paper":"/paper/wechsel-effective-initialization-of-subword-1","slug":"wechsel-effective-initialization-of-subword-1","title":"WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models","date":"2021-12-13","arxiv_id":"2112.06598","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["cpjku/wechsel"],"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":"findings-on-conversation-disentanglement","title":"Findings on Conversation Disentanglement","date":"2021-12-10","arxiv_id":"2112.05346","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-interactions-using-pretrained","slug":"multimodal-interactions-using-pretrained","title":"Multimodal Interactions Using Pretrained Unimodal Models for SIMMC 2.0","date":"2021-12-10","arxiv_id":"2112.05328","n_code_links":1,"syntology":null},{"paper":"/paper/detecting-potentially-harmful-and-protective","slug":"detecting-potentially-harmful-and-protective","title":"Detecting potentially harmful and protective suicide-related content on twitter: A machine learning approach","date":"2021-12-09","arxiv_id":"2112.04796","n_code_links":2,"syntology":null},{"paper":null,"slug":"from-scattered-sources-to-comprehensive","title":"From Scattered Sources to Comprehensive Technology Landscape: A Recommendation-based Retrieval Approach","date":"2021-12-09","arxiv_id":"2112.04810","n_code_links":0,"syntology":null},{"paper":"/paper/semantic-search-as-extractive-paraphrase-span-1","slug":"semantic-search-as-extractive-paraphrase-span-1","title":"Semantic Search as Extractive Paraphrase Span Detection","date":"2021-12-09","arxiv_id":"2112.04886","n_code_links":1,"syntology":null},{"paper":"/paper/improving-language-models-by-retrieving-from","slug":"improving-language-models-by-retrieving-from","title":"Improving language models by retrieving from trillions of tokens","date":"2021-12-08","arxiv_id":"2112.04426","n_code_links":2,"syntology":{"ran":16,"of":23,"n_ran_checked":14,"n_instrument":2,"unverified":7,"pointer_only":3,"phrase":"16 ran (of which 5 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 3 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","official":null}},{"paper":"/paper/jaber-junior-arabic-bert","slug":"jaber-junior-arabic-bert","title":"JABER and SABER: Junior and Senior Arabic BERt","date":"2021-12-08","arxiv_id":"2112.04329","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-transferable-approach-for-partitioning","title":"A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules","date":"2021-12-07","arxiv_id":"2112.04041","n_code_links":0,"syntology":null},{"paper":"/paper/racebert-a-transformer-based-model-for","slug":"racebert-a-transformer-based-model-for","title":"raceBERT -- A Transformer-based Model for Predicting Race and Ethnicity from Names","date":"2021-12-07","arxiv_id":"2112.03807","n_code_links":1,"syntology":null},{"paper":"/paper/bertmap-a-bert-based-ontology-alignment","slug":"bertmap-a-bert-based-ontology-alignment","title":"BERTMap: A BERT-based Ontology Alignment System","date":"2021-12-05","arxiv_id":"2112.02682","n_code_links":1,"syntology":null},{"paper":"/paper/causal-distillation-for-language-models","slug":"causal-distillation-for-language-models","title":"Causal Distillation for Language Models","date":"2021-12-05","arxiv_id":"2112.02505","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["frankaging/Causal-Distill"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/dibert-dependency-injected-bidirectional","slug":"dibert-dependency-injected-bidirectional","title":"DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers","date":"2021-12-05","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/varclr-variable-semantic-representation-pre","slug":"varclr-variable-semantic-representation-pre","title":"VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning","date":"2021-12-05","arxiv_id":"2112.02650","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":["squareslab/varclr"],"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/bridging-pre-trained-models-and-downstream","slug":"bridging-pre-trained-models-and-downstream","title":"Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding","date":"2021-12-04","arxiv_id":"2112.02268","n_code_links":1,"syntology":{"ran":12,"of":13,"n_ran_checked":12,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["wangdeze18/DACL"],"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"]}}},{"paper":null,"slug":"representation-learning-for-conversational","title":"Representation Learning for Conversational Data using Discourse Mutual Information Maximization","date":"2021-12-04","arxiv_id":"2112.05787","n_code_links":0,"syntology":null},{"paper":null,"slug":"unraveling-social-perceptions-behaviors","title":"Unraveling Social Perceptions & Behaviors towards Migrants on Twitter","date":"2021-12-04","arxiv_id":"2112.06642","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-deep-parallel-time-series-relation","title":"A Novel Deep Parallel Time-series Relation Network for Fault Diagnosis","date":"2021-12-03","arxiv_id":"2112.03405","n_code_links":0,"syntology":null},{"paper":null,"slug":"augmenting-customer-support-with-an-nlp-based","title":"Augmenting Customer Support with an NLP-based Receptionist","date":"2021-12-03","arxiv_id":"2112.01959","n_code_links":0,"syntology":null},{"paper":"/paper/given-users-recommendations-based-on-reviews","slug":"given-users-recommendations-based-on-reviews","title":"Given Users Recommendations Based on Reviews on Yelp","date":"2021-12-03","arxiv_id":"2112.01762","n_code_links":1,"syntology":null},{"paper":null,"slug":"nn-lut-neural-approximation-of-non-linear","title":"NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference","date":"2021-12-03","arxiv_id":"2112.02191","n_code_links":0,"syntology":null},{"paper":"/paper/siamese-bert-based-model-for-web-search","slug":"siamese-bert-based-model-for-web-search","title":"Siamese BERT-based Model for Web Search Relevance Ranking Evaluated on a New Czech Dataset","date":"2021-12-03","arxiv_id":"2112.01810","n_code_links":1,"syntology":null},{"paper":"/paper/bevt-bert-pretraining-of-video-transformers","slug":"bevt-bert-pretraining-of-video-transformers","title":"BEVT: BERT Pretraining of Video Transformers","date":"2021-12-02","arxiv_id":"2112.01529","n_code_links":1,"syntology":null},{"paper":"/paper/plsum-generating-pt-br-wikipedia-by","slug":"plsum-generating-pt-br-wikipedia-by","title":"PLSUM: Generating PT-BR Wikipedia by Summarizing Multiple Websites","date":"2021-12-02","arxiv_id":"2112.01591","n_code_links":1,"syntology":null},{"paper":null,"slug":"unsupervised-law-article-mining-based-on-deep","title":"Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code","date":"2021-12-02","arxiv_id":"2112.03033","n_code_links":0,"syntology":null},{"paper":null,"slug":"domain-oriented-language-pre-training-with","title":"Domain-oriented Language Pre-training with Adaptive Hybrid Masking and Optimal Transport Alignment","date":"2021-12-01","arxiv_id":"2112.03024","n_code_links":0,"syntology":null},{"paper":null,"slug":"drone-data-aware-low-rank-compression-for","title":"DRONE: Data-aware Low-rank Compression for Large NLP Models","date":"2021-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ner-bert-a-pre-trained-model-for-low-resource","title":"NER-BERT: A Pre-trained Model for Low-Resource Entity Tagging","date":"2021-12-01","arxiv_id":"2112.00405","n_code_links":0,"syntology":null},{"paper":"/paper/tribert-human-centric-audio-visual","slug":"tribert-human-centric-audio-visual","title":"TriBERT: Human-centric Audio-visual Representation Learning","date":"2021-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"wiki-to-automotive-understanding-the","title":"Wiki to Automotive: Understanding the Distribution Shift and its impact on Named Entity Recognition","date":"2021-12-01","arxiv_id":"2112.00283","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparative-study-of-transformers-on-word","title":"A Comparative Study of Transformers on Word Sense Disambiguation","date":"2021-11-30","arxiv_id":"2111.15417","n_code_links":0,"syntology":null},{"paper":null,"slug":"generating-rich-product-descriptions-for","title":"Generating Rich Product Descriptions for Conversational E-commerce Systems","date":"2021-11-30","arxiv_id":"2111.15298","n_code_links":0,"syntology":null},{"paper":null,"slug":"karl-trans-ner-knowledge-aware-representation","title":"KARL-Trans-NER: Knowledge Aware Representation Learning for Named Entity Recognition using Transformers","date":"2021-11-30","arxiv_id":"2111.15436","n_code_links":0,"syntology":null},{"paper":null,"slug":"nlp-techniques-for-water-quality-analysis-in","title":"NLP Techniques for Water Quality Analysis in Social Media Content","date":"2021-11-30","arxiv_id":"2112.11441","n_code_links":0,"syntology":null},{"paper":"/paper/sentiment-analysis-and-effect-of-covid-19","slug":"sentiment-analysis-and-effect-of-covid-19","title":"Sentiment Analysis and Effect of COVID-19 Pandemic using College SubReddit Data","date":"2021-11-30","arxiv_id":"2112.04351","n_code_links":1,"syntology":null},{"paper":null,"slug":"spaceedit-learning-a-unified-editing-space","title":"SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing","date":"2021-11-30","arxiv_id":"2112.00180","n_code_links":0,"syntology":null},{"paper":null,"slug":"text-classification-problems-via-bert","title":"Text classification problems via BERT embedding method and graph convolutional neural network","date":"2021-11-30","arxiv_id":"2111.15379","n_code_links":0,"syntology":null},{"paper":null,"slug":"customer-sentiment-analysis-using-weak","title":"Customer Sentiment Analysis using Weak Supervision for Customer-Agent Chat","date":"2021-11-29","arxiv_id":"2111.14282","n_code_links":0,"syntology":null},{"paper":"/paper/point-bert-pre-training-3d-point-cloud","slug":"point-bert-pre-training-3d-point-cloud","title":"Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling","date":"2021-11-29","arxiv_id":"2111.14819","n_code_links":3,"syntology":{"ran":5,"of":6,"n_ran_checked":2,"n_instrument":3,"unverified":1,"pointer_only":2,"phrase":"5 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["lulutang0608/Point-BERT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"speech-tasks-relevant-to-sleepiness","title":"Speech Tasks Relevant to Sleepiness Determined with Deep Transfer Learning","date":"2021-11-29","arxiv_id":"2111.14684","n_code_links":0,"syntology":null},{"paper":null,"slug":"tapping-bert-for-preposition-sense","title":"Tapping BERT for Preposition Sense Disambiguation","date":"2021-11-27","arxiv_id":"2111.13972","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-document-coverage-for-relation","title":"Predicting Document Coverage for Relation Extraction","date":"2021-11-26","arxiv_id":"2111.13611","n_code_links":0,"syntology":null},{"paper":null,"slug":"does-constituency-analysis-enhance-domain","title":"Does constituency analysis enhance domain-specific pre-trained BERT models for relation extraction?","date":"2021-11-25","arxiv_id":"2112.02955","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-the-robustness-of-retrieval","slug":"evaluating-the-robustness-of-retrieval","title":"Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators","date":"2021-11-25","arxiv_id":"2111.13057","n_code_links":1,"syntology":null},{"paper":null,"slug":"new-approaches-to-long-document-summarization","title":"New Approaches to Long Document Summarization: Fourier Transform Based Attention in a Transformer Model","date":"2021-11-25","arxiv_id":"2111.15473","n_code_links":0,"syntology":null},{"paper":null,"slug":"probabilistic-impact-score-generation-using","title":"Probabilistic Impact Score Generation using Ktrain-BERT to Identify Hate Words from Twitter Discussions","date":"2021-11-25","arxiv_id":"2111.12939","n_code_links":0,"syntology":null},{"paper":null,"slug":"recommending-multiple-positive-citations-for","title":"Recommending Multiple Positive Citations for Manuscript via Content-Dependent Modeling and Multi-Positive Triplet","date":"2021-11-25","arxiv_id":"2111.12899","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-korean-pretrained-language","title":"Transformer-based Korean Pretrained Language Models: A Survey on Three Years of Progress","date":"2021-11-25","arxiv_id":"2112.03014","n_code_links":0,"syntology":null},{"paper":"/paper/peco-perceptual-codebook-for-bert-pre","slug":"peco-perceptual-codebook-for-bert-pre","title":"PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers","date":"2021-11-24","arxiv_id":"2111.12710","n_code_links":1,"syntology":null},{"paper":"/paper/dabs-a-domain-agnostic-benchmark-for-self","slug":"dabs-a-domain-agnostic-benchmark-for-self","title":"DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning","date":"2021-11-23","arxiv_id":"2111.12062","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["alextamkin/dabs"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/variational-learning-for-unsupervised-1","slug":"variational-learning-for-unsupervised-1","title":"Variational Learning for Unsupervised Knowledge Grounded Dialogs","date":"2021-11-23","arxiv_id":"2112.00653","n_code_links":1,"syntology":null},{"paper":null,"slug":"finding-the-winning-ticket-of-bert-for-binary","title":"Can depth-adaptive BERT perform better on binary classification tasks","date":"2021-11-22","arxiv_id":"2111.10951","n_code_links":0,"syntology":null},{"paper":null,"slug":"does-bert-look-at-sentiment-lexicon","title":"Does BERT look at sentiment lexicon?","date":"2021-11-19","arxiv_id":"2111.10100","n_code_links":0,"syntology":null},{"paper":null,"slug":"lexicon-based-methods-vs-bert-for-text","title":"Lexicon-based Methods vs. BERT for Text Sentiment Analysis","date":"2021-11-19","arxiv_id":"2111.10097","n_code_links":0,"syntology":null},{"paper":"/paper/debertav3-improving-deberta-using-electra","slug":"debertav3-improving-deberta-using-electra","title":"DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing","date":"2021-11-18","arxiv_id":"2111.09543","n_code_links":3,"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":["microsoft/DeBERTa"],"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":"dynamic-tinybert-boost-tinybert-s-inference","title":"Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length","date":"2021-11-18","arxiv_id":"2111.09645","n_code_links":0,"syntology":null},{"paper":"/paper/how-emotionally-stable-is-albert-testing-1","slug":"how-emotionally-stable-is-albert-testing-1","title":"How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task","date":"2021-11-18","arxiv_id":"2111.09612","n_code_links":1,"syntology":null},{"paper":null,"slug":"lanobert-system-log-anomaly-detection-based","title":"LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Model","date":"2021-11-18","arxiv_id":"2111.09564","n_code_links":0,"syntology":null},{"paper":"/paper/robertuito-a-pre-trained-language-model-for","slug":"robertuito-a-pre-trained-language-model-for","title":"RoBERTuito: a pre-trained language model for social media text in Spanish","date":"2021-11-18","arxiv_id":"2111.09453","n_code_links":1,"syntology":{"ran":3,"of":6,"n_ran_checked":3,"n_instrument":0,"unverified":3,"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) · 3 unverified","official":{"repos":["pysentimiento/robertuito"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/the-power-of-selecting-key-blocks-with-local","slug":"the-power-of-selecting-key-blocks-with-local","title":"The Power of Selecting Key Blocks with Local Pre-ranking for Long Document Information Retrieval","date":"2021-11-18","arxiv_id":"2111.09852","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comparative-study-on-transfer-learning-and","title":"A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets","date":"2021-11-16","arxiv_id":"2111.08658","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-flexible-multi-task-model-for-bert-serving-1","title":"A Flexible Multi-Task Model for BERT Serving","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-graph-enhanced-bert-model-for-event","title":"A Graph Enhanced BERT Model for Event Prediction","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-sentence-is-worth-128-pseudo-tokens-a","title":"A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-structured-semantic-reinforcement-method","title":"A Structured Semantic Reinforcement method for Task-Oriented Dialogue","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"adapler-speeding-up-inference-by-adaptive","title":"AdapLeR: Speeding up Inference by Adaptive Length Reduction","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"an-isotropy-analysis-in-the-multilingual-bert-1","title":"An Isotropy Analysis in the Multilingual BERT Embedding Space","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"anna-enhanced-language-representation-for","title":"ANNA: Enhanced Language Representation for Question Answering","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"assessing-the-coherence-modeling-capabilities","title":"Assessing the Coherence Modeling Capabilities of Pretrained Transformer-based Language Models","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/attention-based-multi-hypothesis-fusion-for","slug":"attention-based-multi-hypothesis-fusion-for","title":"Attention-based Multi-hypothesis Fusion for Speech Summarization","date":"2021-11-16","arxiv_id":"2111.08201","n_code_links":2,"syntology":null}],"record_sha256":"8a6c40a9bf5a571a5a30c0494c6c16011e1f7810b06c2a8f8e07f8618cea7a1c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}