{"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/55","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":55,"pages_in_order":71,"rows_per_page":100,"rows":[5401,5500],"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/54","next":"/method/wordpiece/papers/56","papers":[{"paper":"/paper/pre-trained-image-processing-transformer","slug":"pre-trained-image-processing-transformer","title":"Pre-Trained Image Processing Transformer","date":"2020-12-01","arxiv_id":"2012.00364","n_code_links":6,"syntology":null},{"paper":null,"slug":"predicting-stance-change-using-modular","title":"Predicting Stance Change Using Modular Architectures","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"prhlt-upv-at-semeval-2020-task-12-bert-for","title":"PRHLT-UPV at SemEval-2020 Task 12: BERT for Multilingual Offensive Language Detection","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"prhlt-upv-at-semeval-2020-task-8-study-of","title":"PRHLT-UPV at SemEval-2020 Task 8: Study of Multimodal Techniques for Memes Analysis","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"query-distillation-bert-based-distillation","title":"Query Distillation: BERT-based Distillation for Ensemble Ranking","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/retrieving-skills-from-job-descriptions-a","slug":"retrieving-skills-from-job-descriptions-a","title":"Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"robert-a-romanian-bert-model","title":"RoBERT -- A Romanian BERT Model","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-machine-reading-comprehension-by","title":"Robust Machine Reading Comprehension by Learning Soft labels","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"scalable-cross-lingual-treebank-synthesis-for","title":"Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"scientific-keyphrase-identification-and","title":"Scientific Keyphrase Identification and Classification by Pre-Trained Language Models Intermediate Task Transfer Learning","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-supervised-domain-adaptation-for-1","title":"Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sentence-matching-with-syntax-and-semantics","title":"Sentence Matching with Syntax- and Semantics-Aware BERT","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sentiment-analysis-for-emotional-speech","title":"Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/sentix-a-sentiment-aware-pre-trained-model","slug":"sentix-a-sentiment-aware-pre-trained-model","title":"SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"skoltechnlp-at-semeval-2020-task-11-exploring","title":"SkoltechNLP at SemEval-2020 Task 11: Exploring Unsupervised Text Augmentation for Propaganda Detection","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"slice-supersense-based-lightweight","title":"SLICE: Supersense-based Lightweight Interpretable Contextual Embeddings","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"smash-at-semeval-2020-task-7-optimizing-the","title":"Smash at SemEval-2020 Task 7: Optimizing the Hyperparameters of ERNIE 2.0 for Humor Ranking and Rating","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/smyrf-efficient-attention-using-asymmetric-1","slug":"smyrf-efficient-attention-using-asymmetric-1","title":"SMYRF - Efficient Attention using Asymmetric Clustering","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"so-at-semeval-2020-task-7-deeppavlov-logistic","title":"SO at SemEval-2020 Task 7: DeepPavlov Logistic Regression with BERT Embeddings vs SVR at Funniness Evaluation","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sonal-kumari-at-semeval-2020-task-12-social","title":"Sonal.kumari at SemEval-2020 Task 12: Social Media Multilingual Offensive Text Identification and Categorization Using Neural Network Models","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/spanalign-sentence-alignment-method-based-on","slug":"spanalign-sentence-alignment-method-based-on","title":"SpanAlign: Sentence Alignment Method based on Cross-Language Span Prediction and ILP","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"ssn-nlp-at-semeval-2020-task-12-offense","title":"Ssn\\_nlp at SemEval 2020 Task 12: Offense Target Identification in Social Media Using Traditional and Deep Machine Learning Approaches","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ssn-nlp-mlrg-at-semeval-2020-task-12","title":"SSN\\_NLP\\_MLRG at SemEval-2020 Task 12: Offensive Language Identification in English, Danish, Greek Using BERT and Machine Learning Approach","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/stil-simultaneous-slot-filling-translation-1","slug":"stil-simultaneous-slot-filling-translation-1","title":"STIL - Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"su-nlp-at-semeval-2020-task-12-offensive","title":"SU-NLP at SemEval-2020 Task 12: Offensive Language IdentifiCation in Turkish Tweets","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"swagex-at-semeval-2020-task-4-commonsense","title":"SWAGex at SemEval-2020 Task 4: Commonsense Explanation as Next Event Prediction","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"syntactic-graph-convolutional-network-for","title":"Syntactic Graph Convolutional Network for Spoken Language Understanding","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"syntactically-aware-cross-domain-aspect-and","title":"Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"syntax-aware-graph-attention-network-for","title":"Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"t-extmarkers-at-semeval-2020-task-10-emphasis","title":"T\\\"eXtmarkers at SemEval-2020 Task 10: Emphasis Selection with Agreement Dependent Crowd Layers","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"team-disaster-at-semeval-2020-task-11","title":"Team DiSaster at SemEval-2020 Task 11: Combining BERT and Hand-crafted Features for Identifying Propaganda Techniques in News","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"team-solomon-at-semeval-2020-task-4-be","title":"Team Solomon at SemEval-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"teamjust-at-semeval-2020-task-4-commonsense","title":"TeamJUST at SemEval-2020 Task 4: Commonsense Validation and Explanation Using Ensembling Techniques","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"techssn-at-semeval-2020-task-12-offensive","title":"TECHSSN at SemEval-2020 Task 12: Offensive Language Detection Using BERT Embeddings","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"temporalteller-at-semeval-2020-task-1","title":"TemporalTeller at SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection with Temporal Referencing","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"text-classification-by-contrastive-learning","title":"Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer's Disease Detection","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"text-simplification-with-reinforcement","title":"Text Simplification with Reinforcement Learning Using Supervised Rewards on Grammaticality, Meaning Preservation, and Simplicity","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"textlearner-at-semeval-2020-task-10-a","title":"TextLearner at SemEval-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"thenorth-at-semeval-2020-task-12-hate-speech","title":"TheNorth at SemEval-2020 Task 12: Hate Speech Detection Using RoBERTa","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"timbert-toponym-identifier-for-the-medical","title":"TIMBERT: Toponym Identifier For The Medical Domain Based on BERT","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/trainx-named-entity-linking-with-active","slug":"trainx-named-entity-linking-with-active","title":"TrainX -- Named Entity Linking with Active Sampling and Bi-Encoders","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"transformers-at-semeval-2020-task-11","title":"Transformers at SemEval-2020 Task 11: Propaganda Fragment Detection Using Diversified BERT Architectures Based Ensemble Learning","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ttui-at-semeval-2020-task-11-propaganda","title":"TTUI at SemEval-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ui-at-semeval-2020-task-4-commonsense","title":"UI at SemEval-2020 Task 4: Commonsense Validation and Explanation by Exploiting Contradiction","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ujnlp-at-semeval-2020-task-12-detecting","title":"UJNLP at SemEval-2020 Task 12: Detecting Offensive Language Using Bidirectional Transformers","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"umsiforeseer-at-semeval-2020-task-11","title":"UMSIForeseer at SemEval-2020 Task 11: Propaganda Detection by Fine-Tuning BERT with Resampling and Ensemble Learning","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unixlong-at-semeval-2020-task-6-a-joint-model","title":"UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"uor-at-semeval-2020-task-4-pre-trained","title":"UoR at SemEval-2020 Task 4: Pre-trained Sentence Transformer Models for Commonsense Validation and Explanation","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"uzh-at-semeval-2020-task-3-combining-bert","title":"UZH at SemEval-2020 Task 3: Combining BERT with WordNet Sense Embeddings to Predict Graded Word Similarity Changes","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"warren-at-semeval-2020-task-4-albert-and","title":"Warren at SemEval-2020 Task 4: ALBERT and Multi-Task Learning for Commonsense Validation","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"will-go-at-semeval-2020-task-9-an-accurate","title":"Will\\_go at SemEval-2020 Task 9: An Accurate Approach for Sentiment Analysis on Hindi-English Tweets Based on Bert and Pesudo Label Strategy","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"wmd-at-semeval-2020-tasks-7-and-11-assessing","title":"WMD at SemEval-2020 Tasks 7 and 11: Assessing Humor and Propaganda Using Unsupervised Data Augmentation","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"wuy-at-semeval-2020-task-7-combining-bert-and","title":"WUY at SemEval-2020 Task 7: Combining BERT and Naive Bayes-SVM for Humor Assessment in Edited News Headlines","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ynu-hpcc-at-semeval-2020-task-7-using-an","title":"YNU-HPCC at SemEval-2020 Task 7: Using an Ensemble BiGRU Model to Evaluate the Humor of Edited News Titles","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ynu-oxz-at-semeval-2020-task-4-commonsense","title":"YNU-oxz at SemEval-2020 Task 4: Commonsense Validation Using BERT with Bidirectional GRU","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ynutaoxin-at-semeval-2020-task-11","title":"YNUtaoxin at SemEval-2020 Task 11: Identification Fragments of Propaganda Technique by Neural Sequence Labeling Models with Different Tagging Schemes and Pre-trained Language Model","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"you-may-like-this-hotel-because-identifying","title":"You May Like This Hotel Because ...: Identifying Evidence for Explainable Recommendations","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"fake-news-detection-in-social-media-using","title":"Fake News Detection in Social Media using Graph Neural Networks and NLP Techniques: A COVID-19 Use-case","date":"2020-11-30","arxiv_id":"2012.07517","n_code_links":0,"syntology":null},{"paper":"/paper/feature-learning-in-infinite-width-neural","slug":"feature-learning-in-infinite-width-neural","title":"Feature Learning in Infinite-Width Neural Networks","date":"2020-11-30","arxiv_id":"2011.14522","n_code_links":4,"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":["edwardjhu/TP4"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"flood-detection-via-twitter-streams-using","title":"Flood Detection via Twitter Streams using Textual and Visual Features","date":"2020-11-30","arxiv_id":"2011.14944","n_code_links":0,"syntology":null},{"paper":null,"slug":"floods-detection-in-twitter-text-and-images","title":"Floods Detection in Twitter Text and Images","date":"2020-11-30","arxiv_id":"2011.14943","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-modal-detection-of-alzheimer-s-disease","title":"Multi-Modal Detection of Alzheimer's Disease from Speech and Text","date":"2020-11-30","arxiv_id":"2012.00096","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-sentiment-analysis-engine-for","title":"A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media","date":"2020-11-29","arxiv_id":"2011.14280","n_code_links":0,"syntology":null},{"paper":"/paper/coarse-to-fine-memory-matching-for-joint","slug":"coarse-to-fine-memory-matching-for-joint","title":"Coarse-to-Fine Memory Matching for Joint Retrieval and Classification","date":"2020-11-29","arxiv_id":"2012.02287","n_code_links":1,"syntology":null},{"paper":"/paper/improved-semantic-role-labeling-using","slug":"improved-semantic-role-labeling-using","title":"Improved Semantic Role Labeling using Parameterized Neighborhood Memory Adaptation","date":"2020-11-29","arxiv_id":"2011.14459","n_code_links":1,"syntology":null},{"paper":null,"slug":"edgebert-optimizing-on-chip-inference-for","title":"EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference","date":"2020-11-28","arxiv_id":"2011.14203","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-query-target-knowledge-discovery","title":"Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19","date":"2020-11-28","arxiv_id":"2012.04682","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-how-bert-learns-to-identify","slug":"understanding-how-bert-learns-to-identify","title":"An Investigation of Language Model Interpretability via Sentence Editing","date":"2020-11-28","arxiv_id":"2011.14039","n_code_links":2,"syntology":null},{"paper":null,"slug":"chinese-medical-question-answer-matching","title":"Chinese Medical Question Answer Matching Based on Interactive Sentence Representation Learning","date":"2020-11-27","arxiv_id":"2011.13573","n_code_links":0,"syntology":null},{"paper":null,"slug":"core-an-efficient-coarse-refined-training","title":"CoRe: An Efficient Coarse-refined Training Framework for BERT","date":"2020-11-27","arxiv_id":"2011.13633","n_code_links":0,"syntology":null},{"paper":null,"slug":"progressively-stacking-2-0-a-multi-stage-1","title":"Progressively Stacking 2.0: A Multi-stage Layerwise Training Method for BERT Training Speedup","date":"2020-11-27","arxiv_id":"2011.13635","n_code_links":0,"syntology":null},{"paper":"/paper/a-recurrent-vision-and-language-bert-for","slug":"a-recurrent-vision-and-language-bert-for","title":"A Recurrent Vision-and-Language BERT for Navigation","date":"2020-11-26","arxiv_id":"2011.13922","n_code_links":1,"syntology":null},{"paper":null,"slug":"encoding-syntactic-constituency-paths-for","title":"Encoding Syntactic Constituency Paths for Frame-Semantic Parsing with Graph Convolutional Networks","date":"2020-11-26","arxiv_id":"2011.13210","n_code_links":0,"syntology":null},{"paper":"/paper/molecular-representation-learning-with","slug":"molecular-representation-learning-with","title":"Molecular representation learning with language models and domain-relevant auxiliary tasks","date":"2020-11-26","arxiv_id":"2011.13230","n_code_links":2,"syntology":null},{"paper":null,"slug":"transformer-based-models-for-automatic","title":"Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation","date":"2020-11-26","arxiv_id":"2011.13187","n_code_links":0,"syntology":null},{"paper":"/paper/two-stage-transformer-model-for-covid-19-fake","slug":"two-stage-transformer-model-for-covid-19-fake","title":"Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking","date":"2020-11-26","arxiv_id":"2011.13253","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-expand-reinforced-pseudo","title":"Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations","date":"2020-11-25","arxiv_id":"2011.12771","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-deep-neural-networks-with","slug":"enhancing-deep-neural-networks-with","title":"Enhancing deep neural networks with morphological information","date":"2020-11-24","arxiv_id":"2011.12432","n_code_links":2,"syntology":null},{"paper":null,"slug":"experiments-on-transfer-learning","title":"Experiments on transfer learning architectures for biomedical relation extraction","date":"2020-11-24","arxiv_id":"2011.12380","n_code_links":0,"syntology":null},{"paper":"/paper/picking-bert-s-brain-probing-for-linguistic","slug":"picking-bert-s-brain-probing-for-linguistic","title":"Picking BERT's Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis","date":"2020-11-24","arxiv_id":"2011.12073","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":["mlepori1/Picking_BERTs_Brain"],"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":"bi-isca-bidirectional-inter-sentence","title":"Bi-ISCA: Bidirectional Inter-Sentence Contextual Attention Mechanism for Detecting Sarcasm in User Generated Noisy Short Text","date":"2020-11-23","arxiv_id":"2011.11465","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-and-classification-of-mental","title":"Detection and Classification of mental illnesses on social media using RoBERTa","date":"2020-11-23","arxiv_id":"2011.11226","n_code_links":0,"syntology":null},{"paper":"/paper/does-bert-understand-sentiment-leveraging","slug":"does-bert-understand-sentiment-leveraging","title":"Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models","date":"2020-11-23","arxiv_id":"2011.11673","n_code_links":0,"syntology":null},{"paper":"/paper/data-informed-global-sparseness-in-attention","slug":"data-informed-global-sparseness-in-attention","title":"Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks","date":"2020-11-20","arxiv_id":"2012.02030","n_code_links":2,"syntology":null},{"paper":"/paper/fine-tuning-bert-for-sentiment-analysis-of","slug":"fine-tuning-bert-for-sentiment-analysis-of","title":"Fine-Tuning BERT for Sentiment Analysis of Vietnamese Reviews","date":"2020-11-20","arxiv_id":"2011.10426","n_code_links":1,"syntology":null},{"paper":"/paper/multitask-learning-of-negation-and","slug":"multitask-learning-of-negation-and","title":"Multitask Learning of Negation and Speculation using Transformers","date":"2020-11-20","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/onion-a-simple-and-effective-defense-against","slug":"onion-a-simple-and-effective-defense-against","title":"ONION: A Simple and Effective Defense Against Textual Backdoor Attacks","date":"2020-11-20","arxiv_id":"2011.10369","n_code_links":2,"syntology":{"ran":3,"of":4,"n_ran_checked":0,"n_instrument":3,"unverified":1,"pointer_only":0,"phrase":"3 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["thunlp/ONION"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"self-supervised-learning-with-cross-modal","title":"Self-Supervised learning with cross-modal transformers for emotion recognition","date":"2020-11-20","arxiv_id":"2011.10652","n_code_links":0,"syntology":null},{"paper":"/paper/fact-level-extractive-summarization-with","slug":"fact-level-extractive-summarization-with","title":"Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT","date":"2020-11-19","arxiv_id":"2011.09739","n_code_links":1,"syntology":null},{"paper":null,"slug":"diverse-and-non-redundant-answer-set","title":"Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs","date":"2020-11-18","arxiv_id":"2011.09140","n_code_links":0,"syntology":null},{"paper":null,"slug":"do-fine-tuned-commonsense-language-models","title":"Do Fine-tuned Commonsense Language Models Really Generalize?","date":"2020-11-18","arxiv_id":"2011.09159","n_code_links":0,"syntology":null},{"paper":null,"slug":"palomino-ochoa-at-semeval-2020-task-9-robust","title":"Palomino-Ochoa at SemEval-2020 Task 9: Robust System based on Transformer for Code-Mixed Sentiment Classification","date":"2020-11-18","arxiv_id":"2011.09448","n_code_links":0,"syntology":null},{"paper":null,"slug":"tie-your-embeddings-down-cross-modal-latent","title":"Tie Your Embeddings Down: Cross-Modal Latent Spaces for End-to-end Spoken Language Understanding","date":"2020-11-18","arxiv_id":"2011.09044","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-mechanism-transformers-bert-and-gpt","title":"Attention Mechanism, Transformers, BERT, and GPT: Tutorial and Survey","date":"2020-11-17","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"mvp-bert-redesigning-vocabularies-for-chinese-1","title":"MVP-BERT: Redesigning Vocabularies for Chinese BERT and Multi-Vocab Pretraining","date":"2020-11-17","arxiv_id":"2011.08539","n_code_links":0,"syntology":null},{"paper":"/paper/siena-stochastic-multi-expert-neural-patcher","slug":"siena-stochastic-multi-expert-neural-patcher","title":"SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher","date":"2020-11-17","arxiv_id":"2011.08908","n_code_links":1,"syntology":null},{"paper":"/paper/beyond-i-i-d-three-levels-of-generalization","slug":"beyond-i-i-d-three-levels-of-generalization","title":"Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases","date":"2020-11-16","arxiv_id":"2011.07743","n_code_links":1,"syntology":null},{"paper":null,"slug":"don-t-patronize-me-an-annotated-dataset-with","title":"Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities","date":"2020-11-16","arxiv_id":"2011.08320","n_code_links":0,"syntology":null},{"paper":null,"slug":"iit-kgp-at-fincausal-2020-shared-task-1","title":"IIT_kgp at FinCausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports","date":"2020-11-16","arxiv_id":"2011.07670","n_code_links":0,"syntology":null},{"paper":"/paper/it-s-a-thin-line-between-love-and-hate-using","slug":"it-s-a-thin-line-between-love-and-hate-using","title":"It's a Thin Line Between Love and Hate: Using the Echo in Modeling Dynamics of Racist Online Communities","date":"2020-11-16","arxiv_id":"2012.01133","n_code_links":1,"syntology":null}],"record_sha256":"2388f980cfce6615a7dcc6bd00c92cbe513110d95e5456a68fed5c2a3e1f0091","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}