{"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/bert/papers/35","list_of":"/method/bert","method":"BERT","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":35,"pages_in_order":70,"rows_per_page":100,"rows":[3401,3500],"of":6938,"counts":{"archive_papers_tagged":6938,"with_a_code_link":2862,"where_syntology_ran_a_sample":640,"not_listed_spam_title":0,"listed":6938,"listed_where_code_ran":640,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":520,"every_run_a_failure_of_syntologys_instrument":120,"listed_with_a_run_with_no_instrument_failure":520,"listed_every_run_a_failure_of_syntologys_instrument":120,"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/bert","prev":"/method/bert/papers/34","next":"/method/bert/papers/36","papers":[{"paper":null,"slug":"transition-based-abstract-meaning","title":"Transition-based Abstract Meaning Representation Parsing with Contextual Embeddings","date":"2022-06-13","arxiv_id":"2206.06229","n_code_links":0,"syntology":null},{"paper":"/paper/comparative-snippet-generation-1","slug":"comparative-snippet-generation-1","title":"Comparative Snippet Generation","date":"2022-06-11","arxiv_id":"2206.05473","n_code_links":1,"syntology":null},{"paper":null,"slug":"joint-encoder-decoder-self-supervised-pre","title":"Joint Encoder-Decoder Self-Supervised Pre-training for ASR","date":"2022-06-09","arxiv_id":"2206.04465","n_code_links":0,"syntology":null},{"paper":"/paper/traditional-and-context-specific-spam","slug":"traditional-and-context-specific-spam","title":"Traditional and context-specific spam detection in low resource settings","date":"2022-06-09","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/abstraction-not-memory-bert-and-the-english","slug":"abstraction-not-memory-bert-and-the-english","title":"Abstraction not Memory: BERT and the English Article System","date":"2022-06-08","arxiv_id":"2206.04184","n_code_links":1,"syntology":null},{"paper":"/paper/scideberta-learning-deberta-for-science","slug":"scideberta-learning-deberta-for-science","title":"SciDeBERTa: Learning DeBERTa for Science Technology Documents and Fine-Tuning Information Extraction Tasks","date":"2022-06-08","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/always-keep-your-target-in-mind-studying-1","slug":"always-keep-your-target-in-mind-studying-1","title":"Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution","date":"2022-06-07","arxiv_id":"2206.11815","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-empirical-study-of-iot-security-aspects-at","title":"An Empirical Study of IoT Security Aspects at Sentence-Level in Developer Textual Discussions","date":"2022-06-07","arxiv_id":"2206.03079","n_code_links":0,"syntology":null},{"paper":null,"slug":"ochadai-at-semeval-2022-task-2-adversarial","title":"OCHADAI at SemEval-2022 Task 2: Adversarial Training for Multilingual Idiomaticity Detection","date":"2022-06-07","arxiv_id":"2206.03025","n_code_links":0,"syntology":null},{"paper":"/paper/a-computational-psycholinguistic-evaluation","slug":"a-computational-psycholinguistic-evaluation","title":"A computational psycholinguistic evaluation of the syntactic abilities of Galician BERT models at the interface of dependency resolution and training time","date":"2022-06-06","arxiv_id":"2206.02440","n_code_links":1,"syntology":null},{"paper":null,"slug":"spam-detection-using-bert","title":"Spam Detection Using BERT","date":"2022-06-06","arxiv_id":"2206.02443","n_code_links":0,"syntology":null},{"paper":"/paper/what-do-tokens-know-about-their-characters-1","slug":"what-do-tokens-know-about-their-characters-1","title":"What do tokens know about their characters and how do they know it?","date":"2022-06-06","arxiv_id":"2206.02608","n_code_links":1,"syntology":null},{"paper":null,"slug":"sentiment-analysis-of-online-travel-reviews","title":"Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon","date":"2022-06-05","arxiv_id":"2206.02160","n_code_links":0,"syntology":null},{"paper":null,"slug":"speech-detection-task-against-asian-hate-bert","title":"Speech Detection Task Against Asian Hate: BERT the Central, While Data-Centric Studies the Crucial","date":"2022-06-05","arxiv_id":"2206.02114","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparing-performance-of-different","title":"Comparing Performance of Different Linguistically-Backed Word Embeddings for Cyberbullying Detection","date":"2022-06-04","arxiv_id":"2206.01950","n_code_links":0,"syntology":null},{"paper":"/paper/extreme-compression-for-pre-trained","slug":"extreme-compression-for-pre-trained","title":"Extreme Compression for Pre-trained Transformers Made Simple and Efficient","date":"2022-06-04","arxiv_id":"2206.01859","n_code_links":1,"syntology":null},{"paper":"/paper/zeroquant-efficient-and-affordable-post","slug":"zeroquant-efficient-and-affordable-post","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","date":"2022-06-04","arxiv_id":"2206.01861","n_code_links":3,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["microsoft/DeepSpeed"],"state":"official: harvested for another paper","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":null,"slug":"differentially-private-model-compression","title":"Differentially Private Model Compression","date":"2022-06-03","arxiv_id":"2206.01838","n_code_links":0,"syntology":null},{"paper":null,"slug":"extracting-similar-questions-from-naturally","title":"Extracting Similar Questions From Naturally-occurring Business Conversations","date":"2022-06-03","arxiv_id":"2206.01585","n_code_links":0,"syntology":null},{"paper":"/paper/tce-at-qur-an-qa-2022-arabic-language","slug":"tce-at-qur-an-qa-2022-arabic-language","title":"TCE at Qur'an QA 2022: Arabic Language Question Answering Over Holy Qur'an Using a Post-Processed Ensemble of BERT-based Models","date":"2022-06-03","arxiv_id":"2206.01550","n_code_links":1,"syntology":null},{"paper":null,"slug":"mmtm-multi-tasking-multi-decoder-transformer","title":"MMTM: Multi-Tasking Multi-Decoder Transformer for Math Word Problems","date":"2022-06-02","arxiv_id":"2206.01268","n_code_links":0,"syntology":null},{"paper":null,"slug":"assessing-group-level-gender-bias-in","title":"Assessing Group-level Gender Bias in Professional Evaluations: The Case of Medical Student End-of-Shift Feedback","date":"2022-06-01","arxiv_id":"2206.00234","n_code_links":0,"syntology":null},{"paper":"/paper/bert-sort-a-zero-shot-mlm-semantic-encoder-on","slug":"bert-sort-a-zero-shot-mlm-semantic-encoder-on","title":"BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoML","date":"2022-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"order-sensitive-shapley-values-for-evaluating","title":"Order-sensitive Shapley Values for Evaluating Conceptual Soundness of NLP Models","date":"2022-06-01","arxiv_id":"2206.00192","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-unified-framework-for-emotion","title":"A Unified Framework for Emotion Identification and Generation in Dialogues","date":"2022-05-31","arxiv_id":"2205.15513","n_code_links":0,"syntology":null},{"paper":null,"slug":"knowledge-graph-deep-learning-a-case-study-in","title":"Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain","date":"2022-05-31","arxiv_id":"2205.15952","n_code_links":0,"syntology":null},{"paper":"/paper/automatic-short-math-answer-grading-via-in","slug":"automatic-short-math-answer-grading-via-in","title":"Automatic Short Math Answer Grading via In-context Meta-learning","date":"2022-05-30","arxiv_id":"2205.15219","n_code_links":1,"syntology":null},{"paper":"/paper/multi-agent-reinforcement-learning-is-a","slug":"multi-agent-reinforcement-learning-is-a","title":"Multi-Agent Reinforcement Learning is a Sequence Modeling Problem","date":"2022-05-30","arxiv_id":"2205.14953","n_code_links":1,"syntology":null},{"paper":null,"slug":"covid-19-literature-mining-and-retrieval","title":"COVID-19 Literature Mining and Retrieval using Text Mining Approaches","date":"2022-05-29","arxiv_id":"2205.14781","n_code_links":0,"syntology":null},{"paper":"/paper/cped-a-large-scale-chinese-personalized-and-1","slug":"cped-a-large-scale-chinese-personalized-and-1","title":"CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI","date":"2022-05-29","arxiv_id":"2205.14727","n_code_links":1,"syntology":null},{"paper":null,"slug":"micro-expression-recognition-based-on","title":"Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning","date":"2022-05-29","arxiv_id":"2205.14643","n_code_links":0,"syntology":null},{"paper":null,"slug":"urdu-news-article-recommendation-model-using","title":"Urdu News Article Recommendation Model using Natural Language Processing Techniques","date":"2022-05-29","arxiv_id":"2206.11862","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-masked-autoencoders-learn","slug":"multimodal-masked-autoencoders-learn","title":"Multimodal Masked Autoencoders Learn Transferable Representations","date":"2022-05-27","arxiv_id":"2205.14204","n_code_links":3,"syntology":{"ran":12,"of":12,"n_ran_checked":9,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["young-geng/m3ae_public"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"federated-split-bert-for-heterogeneous-text","title":"Federated Split BERT for Heterogeneous Text Classification","date":"2022-05-26","arxiv_id":"2205.13299","n_code_links":0,"syntology":null},{"paper":"/paper/learning-dialogue-representations-from","slug":"learning-dialogue-representations-from","title":"Learning Dialogue Representations from Consecutive Utterances","date":"2022-05-26","arxiv_id":"2205.13568","n_code_links":1,"syntology":null},{"paper":"/paper/leveraging-dependency-grammar-for-fine","slug":"leveraging-dependency-grammar-for-fine","title":"Leveraging Dependency Grammar for Fine-Grained Offensive Language Detection using Graph Convolutional Networks","date":"2022-05-26","arxiv_id":"2205.13164","n_code_links":1,"syntology":null},{"paper":"/paper/the-document-vectors-using-cosine-similarity-1","slug":"the-document-vectors-using-cosine-similarity-1","title":"The Document Vectors Using Cosine Similarity Revisited","date":"2022-05-26","arxiv_id":"2205.13357","n_code_links":1,"syntology":null},{"paper":"/paper/training-and-inference-on-any-order","slug":"training-and-inference-on-any-order","title":"Training and Inference on Any-Order Autoregressive Models the Right Way","date":"2022-05-26","arxiv_id":"2205.13554","n_code_links":1,"syntology":null},{"paper":"/paper/bit-robustly-binarized-multi-distilled","slug":"bit-robustly-binarized-multi-distilled","title":"BiT: Robustly Binarized Multi-distilled Transformer","date":"2022-05-25","arxiv_id":"2205.13016","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/bit"],"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":["listed","official"]}}},{"paper":null,"slug":"lifelong-learning-natural-language-processing","title":"Lifelong Learning Natural Language Processing Approach for Multilingual Data Classification","date":"2022-05-25","arxiv_id":"2206.11867","n_code_links":0,"syntology":null},{"paper":null,"slug":"orca-interpreting-prompted-language-models","title":"ORCA: Interpreting Prompted Language Models via Locating Supporting Data Evidence in the Ocean of Pretraining Data","date":"2022-05-25","arxiv_id":"2205.12600","n_code_links":0,"syntology":null},{"paper":"/paper/robustlr-evaluating-robustness-to-logical","slug":"robustlr-evaluating-robustness-to-logical","title":"RobustLR: Evaluating Robustness to Logical Perturbation in Deductive Reasoning","date":"2022-05-25","arxiv_id":"2205.12598","n_code_links":1,"syntology":{"ran":3,"of":5,"n_ran_checked":3,"n_instrument":0,"unverified":2,"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) · 2 unverified","official":{"repos":["ink-usc/robustlr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"text-to-face-generation-with-stylegan2","title":"Text-to-Face Generation with StyleGAN2","date":"2022-05-25","arxiv_id":"2205.12512","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-understanding-label-regularization","title":"Do we need Label Regularization to Fine-tune Pre-trained Language Models?","date":"2022-05-25","arxiv_id":"2205.12428","n_code_links":0,"syntology":null},{"paper":null,"slug":"train-flat-then-compress-sharpness-aware","title":"Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models","date":"2022-05-25","arxiv_id":"2205.12694","n_code_links":0,"syntology":null},{"paper":"/paper/transcormer-transformer-for-sentence-scoring","slug":"transcormer-transformer-for-sentence-scoring","title":"Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling","date":"2022-05-25","arxiv_id":"2205.12986","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/vulberta-simplified-source-code-pre-training","slug":"vulberta-simplified-source-code-pre-training","title":"VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection","date":"2022-05-25","arxiv_id":"2205.12424","n_code_links":1,"syntology":null},{"paper":"/paper/formulating-few-shot-fine-tuning-towards","slug":"formulating-few-shot-fine-tuning-towards","title":"Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition","date":"2022-05-24","arxiv_id":"2205.11799","n_code_links":1,"syntology":null},{"paper":"/paper/k-12bert-bert-for-k-12-education","slug":"k-12bert-bert-for-k-12-education","title":"K-12BERT: BERT for K-12 education","date":"2022-05-24","arxiv_id":"2205.12335","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-role-of-bidirectionality-in-language","title":"On the Role of Bidirectionality in Language Model Pre-Training","date":"2022-05-24","arxiv_id":"2205.11726","n_code_links":0,"syntology":null},{"paper":"/paper/partial-input-baselines-show-that-nli-models","slug":"partial-input-baselines-show-that-nli-models","title":"Partial-input baselines show that NLI models can ignore context, but they don't","date":"2022-05-24","arxiv_id":"2205.12181","n_code_links":1,"syntology":null},{"paper":"/paper/retromae-pre-training-retrieval-oriented","slug":"retromae-pre-training-retrieval-oriented","title":"RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder","date":"2022-05-24","arxiv_id":"2205.12035","n_code_links":1,"syntology":null},{"paper":"/paper/sparse-mixers-combining-moe-and-mixing-to","slug":"sparse-mixers-combining-moe-and-mixing-to","title":"Sparse Mixers: Combining MoE and Mixing to build a more efficient BERT","date":"2022-05-24","arxiv_id":"2205.12399","n_code_links":1,"syntology":null},{"paper":null,"slug":"word-order-typology-in-multilingual-bert-a","title":"Word-order typology in Multilingual BERT: A case study in subordinate-clause detection","date":"2022-05-24","arxiv_id":"2205.11987","n_code_links":0,"syntology":null},{"paper":"/paper/artificial-intelligence-for-topic-modelling","slug":"artificial-intelligence-for-topic-modelling","title":"Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gita","date":"2022-05-23","arxiv_id":"2205.11020","n_code_links":1,"syntology":null},{"paper":"/paper/kold-korean-offensive-language-dataset","slug":"kold-korean-offensive-language-dataset","title":"KOLD: Korean Offensive Language Dataset","date":"2022-05-23","arxiv_id":"2205.11315","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-ignore-adversarial-attacks-1","title":"Learning to Ignore Adversarial Attacks","date":"2022-05-23","arxiv_id":"2205.11551","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-paradox-of-learning-to-reason-from","slug":"on-the-paradox-of-learning-to-reason-from","title":"On the Paradox of Learning to Reason from Data","date":"2022-05-23","arxiv_id":"2205.11502","n_code_links":1,"syntology":{"ran":7,"of":10,"n_ran_checked":7,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["joshuacnf/paradox-learning2reason"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/outliers-dimensions-that-disrupt-transformers","slug":"outliers-dimensions-that-disrupt-transformers","title":"Outliers Dimensions that Disrupt Transformers Are Driven by Frequency","date":"2022-05-23","arxiv_id":"2205.11380","n_code_links":1,"syntology":{"ran":8,"of":16,"n_ran_checked":8,"n_instrument":0,"unverified":8,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","official":{"repos":["gpucce/outliersvsfreq"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":8,"ran_from_kinds":["official"]}}},{"paper":"/paper/parameter-efficient-sparsity-for-large","slug":"parameter-efficient-sparsity-for-large","title":"Parameter-Efficient Sparsity for Large Language Models Fine-Tuning","date":"2022-05-23","arxiv_id":"2205.11005","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["alibaba/AliceMind","yuchaoli/pst"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/prompt-tuning-for-discriminative-pre-trained-1","slug":"prompt-tuning-for-discriminative-pre-trained-1","title":"Prompt Tuning for Discriminative Pre-trained Language Models","date":"2022-05-23","arxiv_id":"2205.11166","n_code_links":1,"syntology":{"ran":7,"of":12,"n_ran_checked":5,"n_instrument":2,"unverified":5,"pointer_only":0,"phrase":"7 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","official":{"repos":["thunlp/dpt"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"scholarbert-bigger-is-not-always-better","title":"The Diminishing Returns of Masked Language Models to Science","date":"2022-05-23","arxiv_id":"2205.11342","n_code_links":0,"syntology":null},{"paper":null,"slug":"simple-recurrence-improves-masked-language","title":"Simple Recurrence Improves Masked Language Models","date":"2022-05-23","arxiv_id":"2205.11588","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-graph-enhanced-bert-model-for-event-2","title":"A Graph Enhanced BERT Model for Event Prediction","date":"2022-05-22","arxiv_id":"2205.10822","n_code_links":0,"syntology":null},{"paper":"/paper/graphmae-self-supervised-masked-graph","slug":"graphmae-self-supervised-masked-graph","title":"GraphMAE: Self-Supervised Masked Graph Autoencoders","date":"2022-05-22","arxiv_id":"2205.10803","n_code_links":3,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["thudm/graphmae"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/deeper-vs-wider-a-revisit-of-transformer","slug":"deeper-vs-wider-a-revisit-of-transformer","title":"A Study on Transformer Configuration and Training Objective","date":"2022-05-21","arxiv_id":"2205.10505","n_code_links":0,"syntology":null},{"paper":"/paper/pre-training-data-quality-and-quantity-for-a","slug":"pre-training-data-quality-and-quantity-for-a","title":"Pre-training Data Quality and Quantity for a Low-Resource Language: New Corpus and BERT Models for Maltese","date":"2022-05-21","arxiv_id":"2205.10517","n_code_links":1,"syntology":null},{"paper":null,"slug":"current-trends-and-approaches-in-synonyms","title":"Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic","date":"2022-05-20","arxiv_id":"2205.10412","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-extreme-parameter-compression-for-1","slug":"exploring-extreme-parameter-compression-for-1","title":"Exploring Extreme Parameter Compression for Pre-trained Language Models","date":"2022-05-20","arxiv_id":"2205.10036","n_code_links":1,"syntology":null},{"paper":"/paper/pre-training-transformer-models-with-sentence","slug":"pre-training-transformer-models-with-sentence","title":"Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection","date":"2022-05-20","arxiv_id":"2205.10455","n_code_links":0,"syntology":null},{"paper":"/paper/progressive-class-semantic-matching-for-semi-1","slug":"progressive-class-semantic-matching-for-semi-1","title":"Progressive Class Semantic Matching for Semi-supervised Text Classification","date":"2022-05-20","arxiv_id":"2205.10189","n_code_links":1,"syntology":null},{"paper":null,"slug":"arabglossbert-fine-tuning-bert-on-context-1","title":"ArabGlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD","date":"2022-05-19","arxiv_id":"2205.09685","n_code_links":0,"syntology":null},{"paper":"/paper/automated-scoring-for-reading-comprehension","slug":"automated-scoring-for-reading-comprehension","title":"Automated Scoring for Reading Comprehension via In-context BERT Tuning","date":"2022-05-19","arxiv_id":"2205.09864","n_code_links":1,"syntology":null},{"paper":"/paper/overcoming-language-disparity-in-online","slug":"overcoming-language-disparity-in-online","title":"Overcoming Language Disparity in Online Content Classification with Multimodal Learning","date":"2022-05-19","arxiv_id":"2205.09744","n_code_links":1,"syntology":null},{"paper":"/paper/psychiatric-scale-guided-risky-post-screening","slug":"psychiatric-scale-guided-risky-post-screening","title":"Psychiatric Scale Guided Risky Post Screening for Early Detection of Depression","date":"2022-05-19","arxiv_id":"2205.09497","n_code_links":1,"syntology":null},{"paper":"/paper/lerac-learning-rate-curriculum","slug":"lerac-learning-rate-curriculum","title":"Learning Rate Curriculum","date":"2022-05-18","arxiv_id":"2205.09180","n_code_links":1,"syntology":null},{"paper":"/paper/persian-natural-language-inference-a-meta-1","slug":"persian-natural-language-inference-a-meta-1","title":"Persian Natural Language Inference: A Meta-learning approach","date":"2022-05-18","arxiv_id":"2205.08755","n_code_links":1,"syntology":null},{"paper":null,"slug":"feature-aggregation-in-zero-shot-cross","title":"Feature Aggregation in Zero-Shot Cross-Lingual Transfer Using Multilingual BERT","date":"2022-05-17","arxiv_id":"2205.08497","n_code_links":0,"syntology":null},{"paper":null,"slug":"samu-xlsr-semantically-aligned-multimodal","title":"SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation","date":"2022-05-17","arxiv_id":"2205.08180","n_code_links":0,"syntology":null},{"paper":"/paper/semi-fnd-stacked-ensemble-based-multimodal","slug":"semi-fnd-stacked-ensemble-based-multimodal","title":"SEMI-FND: Stacked Ensemble Based Multimodal Inference For Faster Fake News Detection","date":"2022-05-17","arxiv_id":"2205.08159","n_code_links":0,"syntology":null},{"paper":"/paper/viralbert-a-user-focused-bert-based-approach","slug":"viralbert-a-user-focused-bert-based-approach","title":"ViralBERT: A User Focused BERT-Based Approach to Virality Prediction","date":"2022-05-17","arxiv_id":"2206.10298","n_code_links":1,"syntology":null},{"paper":"/paper/chemical-transformer-compression-for","slug":"chemical-transformer-compression-for","title":"Chemical transformer compression for accelerating both training and inference of molecular modeling","date":"2022-05-16","arxiv_id":"2205.07582","n_code_links":1,"syntology":null},{"paper":null,"slug":"harnessing-multilingual-resources-to-question","title":"Harnessing Multilingual Resources to Question Answering in Arabic","date":"2022-05-16","arxiv_id":"2205.08024","n_code_links":0,"syntology":null},{"paper":null,"slug":"discovering-latent-concepts-learned-in-bert-1","title":"Discovering Latent Concepts Learned in BERT","date":"2022-05-15","arxiv_id":"2205.07237","n_code_links":0,"syntology":null},{"paper":"/paper/learning-lip-based-audio-visual-speaker","slug":"learning-lip-based-audio-visual-speaker","title":"Learning Lip-Based Audio-Visual Speaker Embeddings with AV-HuBERT","date":"2022-05-15","arxiv_id":"2205.07180","n_code_links":1,"syntology":null},{"paper":null,"slug":"fake-news-quick-detection-on-dynamic","title":"Fake News Quick Detection on Dynamic Heterogeneous Information Networks","date":"2022-05-14","arxiv_id":"2205.07039","n_code_links":0,"syntology":null},{"paper":"/paper/naturalistic-causal-probing-for-morpho-syntax","slug":"naturalistic-causal-probing-for-morpho-syntax","title":"Naturalistic Causal Probing for Morpho-Syntax","date":"2022-05-14","arxiv_id":"2205.07043","n_code_links":1,"syntology":null},{"paper":"/paper/a-study-of-the-attention-abnormality-in-1","slug":"a-study-of-the-attention-abnormality-in-1","title":"A Study of the Attention Abnormality in Trojaned BERTs","date":"2022-05-13","arxiv_id":"2205.08305","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":["weimin17/attention_abnormality_in_trojaned_berts"],"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":"improving-contextual-representation-with-1","title":"Improving Contextual Representation with Gloss Regularized Pre-training","date":"2022-05-13","arxiv_id":"2205.06603","n_code_links":0,"syntology":null},{"paper":null,"slug":"apptek-s-submission-to-the-iwslt-2022","title":"AppTek's Submission to the IWSLT 2022 Isometric Spoken Language Translation Task","date":"2022-05-12","arxiv_id":"2205.05807","n_code_links":0,"syntology":null},{"paper":null,"slug":"is-the-computation-of-abstract-sameness","title":"Is the Computation of Abstract Sameness Relations Human-Like in Neural Language Models?","date":"2022-05-12","arxiv_id":"2205.06149","n_code_links":0,"syntology":null},{"paper":null,"slug":"ner-mqmrc-formulating-named-entity","title":"NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension","date":"2022-05-12","arxiv_id":"2205.05904","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-time-varying-study-of-chinese-investor","title":"A time-varying study of Chinese investor sentiment, stock market liquidity and volatility: Based on deep learning BERT model and TVP-VAR model","date":"2022-05-11","arxiv_id":"2205.05719","n_code_links":0,"syntology":null},{"paper":"/paper/query-based-keyphrase-extraction-from-long","slug":"query-based-keyphrase-extraction-from-long","title":"Query-Based Keyphrase Extraction from Long Documents","date":"2022-05-11","arxiv_id":"2205.05391","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-based-chinese-text-sentiment","title":"Deep learning based Chinese text sentiment mining and stock market correlation research","date":"2022-05-10","arxiv_id":"2205.04743","n_code_links":0,"syntology":null},{"paper":"/paper/problems-with-cosine-as-a-measure-of","slug":"problems-with-cosine-as-a-measure-of","title":"Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words","date":"2022-05-10","arxiv_id":"2205.05092","n_code_links":2,"syntology":null},{"paper":null,"slug":"automated-evaluation-for-student","title":"Automated Evaluation for Student Argumentative Writing: A Survey","date":"2022-05-09","arxiv_id":"2205.04083","n_code_links":0,"syntology":null},{"paper":"/paper/long-document-re-ranking-with-modular-re","slug":"long-document-re-ranking-with-modular-re","title":"Long Document Re-ranking with Modular Re-ranker","date":"2022-05-09","arxiv_id":"2205.04275","n_code_links":1,"syntology":null},{"paper":null,"slug":"research-on-the-correlation-between-text","title":"Research on the correlation between text emotion mining and stock market based on deep learning","date":"2022-05-09","arxiv_id":"2205.06675","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-use-of-bert-for-automated-essay","slug":"on-the-use-of-bert-for-automated-essay","title":"On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation","date":"2022-05-08","arxiv_id":"2205.03835","n_code_links":1,"syntology":null}],"record_sha256":"121eaa5ab7ed06f93e0d64775357a05d16f52563f5a8569db624ac46935b829d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}