{"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/layer-normalization/papers/227","list_of":"/method/layer-normalization","method":"Layer Normalization","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":227,"pages_in_order":250,"rows_per_page":100,"rows":[22601,22700],"of":24980,"counts":{"archive_papers_tagged":24980,"with_a_code_link":11273,"where_syntology_ran_a_sample":3471,"not_listed_spam_title":0,"listed":24980,"listed_where_code_ran":3471,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2923,"every_run_a_failure_of_syntologys_instrument":548,"listed_with_a_run_with_no_instrument_failure":2923,"listed_every_run_a_failure_of_syntologys_instrument":548,"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/layer-normalization","prev":"/method/layer-normalization/papers/226","next":"/method/layer-normalization/papers/228","papers":[{"paper":null,"slug":"dynamics-of-feed-forward-induced-interference","title":"Dynamics of feed forward induced interference training","date":"2020-08-24","arxiv_id":"2008.11111","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-dialogue-transformer","title":"End to End Dialogue Transformer","date":"2020-08-24","arxiv_id":"2008.10392","n_code_links":0,"syntology":null},{"paper":"/paper/knowledge-empowered-representation-learning","slug":"knowledge-empowered-representation-learning","title":"Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources","date":"2020-08-24","arxiv_id":"2008.10327","n_code_links":1,"syntology":null},{"paper":null,"slug":"prediction-of-icd-codes-with-clinical-bert","title":"Prediction of ICD Codes with Clinical BERT Embeddings and Text Augmentation with Label Balancing using MIMIC-III","date":"2020-08-24","arxiv_id":"2008.10492","n_code_links":0,"syntology":null},{"paper":null,"slug":"syrapropa-at-semeval-2020-task-11-bert-based","title":"syrapropa at SemEval-2020 Task 11: BERT-based Models Design For Propagandistic Technique and Span Detection","date":"2020-08-24","arxiv_id":"2008.10163","n_code_links":0,"syntology":null},{"paper":null,"slug":"two-stages-approach-for-tweet-engagement","title":"Two Stages Approach for Tweet Engagement Prediction","date":"2020-08-24","arxiv_id":"2008.10419","n_code_links":0,"syntology":null},{"paper":"/paper/ynu-hpcc-at-semeval-2020-task-11-lstm-network","slug":"ynu-hpcc-at-semeval-2020-task-11-lstm-network","title":"YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles","date":"2020-08-24","arxiv_id":"2008.10166","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":["daojiaxu/semeval_11"],"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":null,"slug":"applications-of-bert-based-sequence-tagging","title":"Applications of BERT Based Sequence Tagging Models on Chinese Medical Text Attributes Extraction","date":"2020-08-22","arxiv_id":"2008.09740","n_code_links":0,"syntology":null},{"paper":"/paper/cyberwalle-at-semeval-2020-task-11-an","slug":"cyberwalle-at-semeval-2020-task-11-an","title":"CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection","date":"2020-08-22","arxiv_id":"2008.09859","n_code_links":1,"syntology":null},{"paper":"/paper/duth-at-semeval-2020-task-11-bert-with-entity","slug":"duth-at-semeval-2020-task-11-bert-with-entity","title":"DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda Classification","date":"2020-08-22","arxiv_id":"2008.09894","n_code_links":1,"syntology":null},{"paper":"/paper/fat-albert-finding-answers-in-large-texts","slug":"fat-albert-finding-answers-in-large-texts","title":"FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERT","date":"2020-08-22","arxiv_id":"2009.01004","n_code_links":1,"syntology":null},{"paper":"/paper/hinglishnlp-fine-tuned-language-models-for","slug":"hinglishnlp-fine-tuned-language-models-for","title":"HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection","date":"2020-08-22","arxiv_id":"2008.09820","n_code_links":2,"syntology":null},{"paper":"/paper/identity-aware-multi-sentence-video","slug":"identity-aware-multi-sentence-video","title":"Identity-Aware Multi-Sentence Video Description","date":"2020-08-22","arxiv_id":"2008.09791","n_code_links":1,"syntology":null},{"paper":"/paper/abstractive-summarization-of-spoken","slug":"abstractive-summarization-of-spoken","title":"Abstractive Summarization of Spoken andWritten Instructions with BERT","date":"2020-08-21","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"adapting-event-extractors-to-medical-data","title":"Adapting Event Extractors to Medical Data: Bridging the Covariate Shift","date":"2020-08-21","arxiv_id":"2008.09266","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-experimental-study-of-deep-neural-network","title":"An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension","date":"2020-08-20","arxiv_id":"2008.08810","n_code_links":0,"syntology":null},{"paper":"/paper/awnet-attentive-wavelet-network-for-image-isp","slug":"awnet-attentive-wavelet-network-for-image-isp","title":"AWNet: Attentive Wavelet Network for Image ISP","date":"2020-08-20","arxiv_id":"2008.09228","n_code_links":1,"syntology":null},{"paper":"/paper/lite-training-strategies-for-portuguese","slug":"lite-training-strategies-for-portuguese","title":"Lite Training Strategies for Portuguese-English and English-Portuguese Translation","date":"2020-08-20","arxiv_id":"2008.08769","n_code_links":1,"syntology":null},{"paper":"/paper/parade-passage-representation-aggregation-for","slug":"parade-passage-representation-aggregation-for","title":"PARADE: Passage Representation Aggregation for Document Reranking","date":"2020-08-20","arxiv_id":"2008.09093","n_code_links":1,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"0 ran · 3 unverified","official":{"repos":["canjiali/PARADE"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"paper":"/paper/ptt5-pretraining-and-validating-the-t5-model","slug":"ptt5-pretraining-and-validating-the-t5-model","title":"PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data","date":"2020-08-20","arxiv_id":"2008.09144","n_code_links":3,"syntology":null},{"paper":"/paper/top2vec-distributed-representations-of-topics","slug":"top2vec-distributed-representations-of-topics","title":"Top2Vec: Distributed Representations of Topics","date":"2020-08-19","arxiv_id":"2008.09470","n_code_links":2,"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":{"repos":["ddangelov/Top2Vec"],"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":"uob-at-semeval-2020-task-12-boosting-bert","title":"UoB at SemEval-2020 Task 12: Boosting BERT with Corpus Level Information","date":"2020-08-19","arxiv_id":"2008.08547","n_code_links":0,"syntology":null},{"paper":"/paper/are-neural-open-domain-dialog-systems-robust","slug":"are-neural-open-domain-dialog-systems-robust","title":"Are Neural Open-Domain Dialog Systems Robust to Speech Recognition Errors in the Dialog History? An Empirical Study","date":"2020-08-18","arxiv_id":"2008.07683","n_code_links":1,"syntology":null},{"paper":null,"slug":"estimation-of-causal-effects-of-multiple","title":"Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes","date":"2020-08-18","arxiv_id":"2008.07687","n_code_links":0,"syntology":null},{"paper":"/paper/glancing-transformer-for-non-autoregressive","slug":"glancing-transformer-for-non-autoregressive","title":"Glancing Transformer for Non-Autoregressive Neural Machine Translation","date":"2020-08-18","arxiv_id":"2008.07905","n_code_links":2,"syntology":null},{"paper":null,"slug":"ranking-clarification-questions-via-natural","title":"Ranking Clarification Questions via Natural Language Inference","date":"2020-08-18","arxiv_id":"2008.07688","n_code_links":0,"syntology":null},{"paper":"/paper/very-deep-transformers-for-neural-machine","slug":"very-deep-transformers-for-neural-machine","title":"Very Deep Transformers for Neural Machine Translation","date":"2020-08-18","arxiv_id":"2008.07772","n_code_links":4,"syntology":{"ran":8,"of":9,"n_ran_checked":3,"n_instrument":5,"unverified":1,"pointer_only":3,"phrase":"8 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","official":{"repos":["namisan/exdeep-nmt"],"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":"generative-models-are-unsupervised-predictors","title":"Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study","date":"2020-08-17","arxiv_id":"2008.13533","n_code_links":0,"syntology":null},{"paper":null,"slug":"narrative-interpolation-for-generating-and","title":"Narrative Interpolation for Generating and Understanding Stories","date":"2020-08-17","arxiv_id":"2008.07466","n_code_links":0,"syntology":null},{"paper":"/paper/spatial-temporal-transformer-network-for","slug":"spatial-temporal-transformer-network-for","title":"Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks","date":"2020-08-17","arxiv_id":"2008.07404","n_code_links":1,"syntology":null},{"paper":null,"slug":"stock-index-prediction-with-multi-task","title":"Stock Index Prediction with Multi-task Learning and Word Polarity Over Time","date":"2020-08-17","arxiv_id":"2008.07605","n_code_links":0,"syntology":null},{"paper":null,"slug":"adding-recurrence-to-pretrained-transformers","title":"Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size","date":"2020-08-16","arxiv_id":"2008.07027","n_code_links":0,"syntology":null},{"paper":null,"slug":"dcr-net-a-deep-co-interactive-relation","title":"DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification","date":"2020-08-16","arxiv_id":"2008.06914","n_code_links":0,"syntology":null},{"paper":"/paper/devlbert-learning-deconfounded-visio","slug":"devlbert-learning-deconfounded-visio","title":"DeVLBert: Learning Deconfounded Visio-Linguistic Representations","date":"2020-08-16","arxiv_id":"2008.06884","n_code_links":1,"syntology":null},{"paper":null,"slug":"topicbert-a-transformer-transfer-learning","title":"TopicBERT: A Transformer transfer learning based memory-graph approach for multimodal streaming social media topic detection","date":"2020-08-16","arxiv_id":"2008.06877","n_code_links":0,"syntology":null},{"paper":null,"slug":"finding-fast-transformers-one-shot-neural","title":"Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition","date":"2020-08-15","arxiv_id":"2008.06808","n_code_links":0,"syntology":null},{"paper":"/paper/jointly-fine-tuning-bert-like-self-supervised","slug":"jointly-fine-tuning-bert-like-self-supervised","title":"Jointly Fine-Tuning \"BERT-like\" Self Supervised Models to Improve Multimodal Speech Emotion Recognition","date":"2020-08-15","arxiv_id":"2008.06682","n_code_links":1,"syntology":null},{"paper":"/paper/jointly-fine-tuning-bert-like-self-supervised-1","slug":"jointly-fine-tuning-bert-like-self-supervised-1","title":"Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion Recognition","date":"2020-08-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"a-hybrid-bert-and-lightgbm-based-model-for","title":"A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter","date":"2020-08-14","arxiv_id":"2008.06176","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptable-multi-domain-language-model-for","title":"Adaptable Multi-Domain Language Model for Transformer ASR","date":"2020-08-14","arxiv_id":"2008.06208","n_code_links":0,"syntology":null},{"paper":null,"slug":"hate-speech-detection-and-racial-bias","title":"Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model","date":"2020-08-14","arxiv_id":"2008.06460","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-models-as-few-shot-learner-for-task","title":"Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems","date":"2020-08-14","arxiv_id":"2008.06239","n_code_links":0,"syntology":null},{"paper":"/paper/a-community-powered-search-of-machine","slug":"a-community-powered-search-of-machine","title":"A community-powered search of machine learning strategy space to find NMR property prediction models","date":"2020-08-13","arxiv_id":"2008.05994","n_code_links":1,"syntology":null},{"paper":"/paper/andes-at-semeval-2020-task-12-a-jointly","slug":"andes-at-semeval-2020-task-12-a-jointly","title":"ANDES at SemEval-2020 Task 12: A jointly-trained BERT multilingual model for offensive language detection","date":"2020-08-13","arxiv_id":"2008.06408","n_code_links":1,"syntology":null},{"paper":null,"slug":"conv-transformer-transducer-low-latency-low","title":"Conv-Transformer Transducer: Low Latency, Low Frame Rate, Streamable End-to-End Speech Recognition","date":"2020-08-13","arxiv_id":"2008.05750","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-contextual-perception-and","title":"End-to-end Contextual Perception and Prediction with Interaction Transformer","date":"2020-08-13","arxiv_id":"2008.05927","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-scale-transfer-learning-for-low","title":"Large-scale Transfer Learning for Low-resource Spoken Language Understanding","date":"2020-08-13","arxiv_id":"2008.05671","n_code_links":0,"syntology":null},{"paper":"/paper/mice-mining-idioms-with-contextual-embeddings","slug":"mice-mining-idioms-with-contextual-embeddings","title":"MICE: Mining Idioms with Contextual Embeddings","date":"2020-08-13","arxiv_id":"2008.05759","n_code_links":1,"syntology":null},{"paper":"/paper/mmm-exploring-conditional-multi-track-music","slug":"mmm-exploring-conditional-multi-track-music","title":"MMM : Exploring Conditional Multi-Track Music Generation with the Transformer","date":"2020-08-13","arxiv_id":"2008.06048","n_code_links":3,"syntology":{"ran":9,"of":9,"n_ran_checked":9,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"9 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; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"compression-of-deep-learning-models-for-text","title":"Compression of Deep Learning Models for Text: A Survey","date":"2020-08-12","arxiv_id":"2008.05221","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-the-impact-of-knowledge-graph","slug":"evaluating-the-impact-of-knowledge-graph","title":"Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models","date":"2020-08-12","arxiv_id":"2008.05190","n_code_links":1,"syntology":null},{"paper":"/paper/fine-grained-visual-textual-alignment-for","slug":"fine-grained-visual-textual-alignment-for","title":"Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders","date":"2020-08-12","arxiv_id":"2008.05231","n_code_links":1,"syntology":{"ran":14,"of":16,"n_ran_checked":13,"n_instrument":1,"unverified":2,"pointer_only":2,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 1 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["mesnico/TERAN"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"variance-reduced-language-pretraining-via-a","title":"Variance-reduced Language Pretraining via a Mask Proposal Network","date":"2020-08-12","arxiv_id":"2008.05333","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-lexical-a-semantic-retrieval-framework","title":"Beyond Lexical: A Semantic Retrieval Framework for Textual SearchEngine","date":"2020-08-10","arxiv_id":"2008.03917","n_code_links":0,"syntology":null},{"paper":null,"slug":"does-bert-solve-commonsense-task-via","title":"On Commonsense Cues in BERT for Solving Commonsense Tasks","date":"2020-08-10","arxiv_id":"2008.03945","n_code_links":0,"syntology":null},{"paper":"/paper/firebert-hardening-bert-based-classifiers","slug":"firebert-hardening-bert-based-classifiers","title":"FireBERT: Hardening BERT-based classifiers against adversarial attack","date":"2020-08-10","arxiv_id":"2008.04203","n_code_links":1,"syntology":null},{"paper":null,"slug":"ganbert-generative-adversarial-networks-with","title":"GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis","date":"2020-08-10","arxiv_id":"2008.04393","n_code_links":0,"syntology":null},{"paper":"/paper/kr-bert-a-small-scale-korean-specific","slug":"kr-bert-a-small-scale-korean-specific","title":"KR-BERT: A Small-Scale Korean-Specific Language Model","date":"2020-08-10","arxiv_id":"2008.03979","n_code_links":1,"syntology":null},{"paper":null,"slug":"navigating-language-models-with-synthetic","title":"Navigating Human Language Models with Synthetic Agents","date":"2020-08-10","arxiv_id":"2008.04162","n_code_links":0,"syntology":null},{"paper":"/paper/distilling-the-knowledge-of-bert-for-sequence","slug":"distilling-the-knowledge-of-bert-for-sequence","title":"Distilling the Knowledge of BERT for Sequence-to-Sequence ASR","date":"2020-08-09","arxiv_id":"2008.03822","n_code_links":1,"syntology":null},{"paper":"/paper/fast-and-accurate-neural-crf-constituency-1","slug":"fast-and-accurate-neural-crf-constituency-1","title":"Fast and Accurate Neural CRF Constituency Parsing","date":"2020-08-09","arxiv_id":"2008.03736","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yzhangcs/crfpar"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["found_in_text","official"]}}},{"paper":"/paper/pretraining-techniques-for-sequence-to","slug":"pretraining-techniques-for-sequence-to","title":"Pretraining Techniques for Sequence-to-Sequence Voice Conversion","date":"2020-08-07","arxiv_id":"2008.03088","n_code_links":2,"syntology":null},{"paper":null,"slug":"semeval-2020-task-10-emphasis-selection-for","title":"SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media","date":"2020-08-07","arxiv_id":"2008.03274","n_code_links":0,"syntology":null},{"paper":"/paper/aschern-at-semeval-2020-task-11-it-takes","slug":"aschern-at-semeval-2020-task-11-it-takes","title":"aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning","date":"2020-08-06","arxiv_id":"2008.02837","n_code_links":1,"syntology":null},{"paper":"/paper/convbert-improving-bert-with-span-based","slug":"convbert-improving-bert-with-span-based","title":"ConvBERT: Improving BERT with Span-based Dynamic Convolution","date":"2020-08-06","arxiv_id":"2008.02496","n_code_links":8,"syntology":null},{"paper":"/paper/detext-a-deep-text-ranking-framework-with","slug":"detext-a-deep-text-ranking-framework-with","title":"DeText: A Deep Text Ranking Framework with BERT","date":"2020-08-06","arxiv_id":"2008.02460","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["linkedin/detext"],"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":"/paper/question-and-answer-test-train-overlap-in","slug":"question-and-answer-test-train-overlap-in","title":"Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets","date":"2020-08-06","arxiv_id":"2008.02637","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/QA-Overlap"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"6veclm-language-modeling-in-vector-space-for","title":"6VecLM: Language Modeling in Vector Space for IPv6 Target Generation","date":"2020-08-05","arxiv_id":"2008.02213","n_code_links":0,"syntology":null},{"paper":"/paper/designing-the-business-conversation-corpus-1","slug":"designing-the-business-conversation-corpus-1","title":"Designing the Business Conversation Corpus","date":"2020-08-05","arxiv_id":"2008.01940","n_code_links":1,"syntology":null},{"paper":"/paper/trove-ontology-driven-weak-supervision-for","slug":"trove-ontology-driven-weak-supervision-for","title":"Ontology-driven weak supervision for clinical entity classification in electronic health records","date":"2020-08-05","arxiv_id":"2008.01972","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":{"repos":["som-shahlab/trove"],"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":"/paper/i-aid-identifying-actionable-information-from","slug":"i-aid-identifying-actionable-information-from","title":"I-AID: Identifying Actionable Information from Disaster-related Tweets","date":"2020-08-04","arxiv_id":"2008.13544","n_code_links":1,"syntology":null},{"paper":"/paper/nlpdove-at-semeval-2020-task-12-improving","slug":"nlpdove-at-semeval-2020-task-12-improving","title":"NLPDove at SemEval-2020 Task 12: Improving Offensive Language Detection with Cross-lingual Transfer","date":"2020-08-04","arxiv_id":"2008.01354","n_code_links":1,"syntology":null},{"paper":null,"slug":"select-extract-and-generate-neural-keyphrase","title":"Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention","date":"2020-08-04","arxiv_id":"2008.01739","n_code_links":0,"syntology":null},{"paper":null,"slug":"taking-notes-on-the-fly-helps-bert-pre","title":"Taking Notes on the Fly Helps BERT Pre-training","date":"2020-08-04","arxiv_id":"2008.01466","n_code_links":0,"syntology":null},{"paper":"/paper/the-jazz-transformer-on-the-front-line","slug":"the-jazz-transformer-on-the-front-line","title":"The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-composed Music through Quantitative Measures","date":"2020-08-04","arxiv_id":"2008.01307","n_code_links":2,"syntology":{"ran":13,"of":14,"n_ran_checked":13,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["slSeanWU/MusDr","slSeanWU/jazz_transformer"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/improving-one-stage-visual-grounding-by","slug":"improving-one-stage-visual-grounding-by","title":"Improving One-stage Visual Grounding by Recursive Sub-query Construction","date":"2020-08-03","arxiv_id":"2008.01059","n_code_links":1,"syntology":{"ran":20,"of":22,"n_ran_checked":19,"n_instrument":1,"unverified":2,"pointer_only":3,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 1 honoured, 0 violated, 18 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["zyang-ur/ReSC"],"state":"official (archive's flag): 20 ran","n_ran":20,"n_constructed":0,"n_ran_no_instrument_failure":19,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"lt-helsinki-at-semeval-2020-task-12","title":"LT@Helsinki at SemEval-2020 Task 12: Multilingual or language-specific BERT?","date":"2020-08-03","arxiv_id":"2008.00805","n_code_links":0,"syntology":null},{"paper":null,"slug":"musicoder-a-universal-music-acoustic-encoder","title":"MusiCoder: A Universal Music-Acoustic Encoder Based on Transformers","date":"2020-08-03","arxiv_id":"2008.00781","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-attention-encoding-and-pooling-for","title":"Self-attention encoding and pooling for speaker recognition","date":"2020-08-03","arxiv_id":"2008.01077","n_code_links":0,"syntology":null},{"paper":"/paper/seqdialn-sequential-visual-dialog-networks-in","slug":"seqdialn-sequential-visual-dialog-networks-in","title":"SeqDialN: Sequential Visual Dialog Networks in Joint Visual-Linguistic Representation Space","date":"2020-08-02","arxiv_id":"2008.00397","n_code_links":1,"syntology":null},{"paper":"/paper/the-chess-transformer-mastering-play-using","slug":"the-chess-transformer-mastering-play-using","title":"The Chess Transformer: Mastering Play using Generative Language Models","date":"2020-08-02","arxiv_id":"2008.04057","n_code_links":2,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"multi-node-bert-pretraining-cost-efficient","title":"Multi-node Bert-pretraining: Cost-efficient Approach","date":"2020-08-01","arxiv_id":"2008.00177","n_code_links":0,"syntology":null},{"paper":"/paper/stochastic-fine-grained-labeling-of-multi","slug":"stochastic-fine-grained-labeling-of-multi","title":"Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition","date":"2020-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/trojaning-language-models-for-fun-and-profit","slug":"trojaning-language-models-for-fun-and-profit","title":"Trojaning Language Models for Fun and Profit","date":"2020-08-01","arxiv_id":"2008.00312","n_code_links":1,"syntology":null},{"paper":"/paper/domain-specific-language-model-pretraining","slug":"domain-specific-language-model-pretraining","title":"Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing","date":"2020-07-31","arxiv_id":"2007.15779","n_code_links":2,"syntology":null},{"paper":"/paper/language-modelling-for-source-code-with","slug":"language-modelling-for-source-code-with","title":"Language Modelling for Source Code with Transformer-XL","date":"2020-07-31","arxiv_id":"2007.15813","n_code_links":1,"syntology":null},{"paper":null,"slug":"model-reduction-of-shallow-cnn-model-for","title":"Model Reduction of Shallow CNN Model for Reliable Deployment of Information Extraction from Medical Reports","date":"2020-07-31","arxiv_id":"2008.01572","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-learning-universal-representations-across","title":"On Learning Universal Representations Across Languages","date":"2020-07-31","arxiv_id":"2007.15960","n_code_links":0,"syntology":null},{"paper":"/paper/tweepfake-about-detecting-deepfake-tweets","slug":"tweepfake-about-detecting-deepfake-tweets","title":"TweepFake: about Detecting Deepfake Tweets","date":"2020-07-31","arxiv_id":"2008.00036","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-multi-view-spatiotemporal-virtual-graph","title":"Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction","date":"2020-07-30","arxiv_id":"2007.15189","n_code_links":0,"syntology":null},{"paper":null,"slug":"depressive-drug-abusive-or-informative","title":"Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak","date":"2020-07-30","arxiv_id":"2007.15209","n_code_links":0,"syntology":null},{"paper":"/paper/interpretable-contextual-team-aware-item","slug":"interpretable-contextual-team-aware-item","title":"Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games","date":"2020-07-30","arxiv_id":"2007.15236","n_code_links":1,"syntology":null},{"paper":"/paper/mkqa-a-linguistically-diverse-benchmark-for","slug":"mkqa-a-linguistically-diverse-benchmark-for","title":"MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering","date":"2020-07-30","arxiv_id":"2007.15207","n_code_links":2,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["apple/ml-mkqa"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/what-does-bert-know-about-books-movies-and","slug":"what-does-bert-know-about-books-movies-and","title":"What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation","date":"2020-07-30","arxiv_id":"2007.15356","n_code_links":1,"syntology":null},{"paper":"/paper/composer-style-classification-of-piano-sheet","slug":"composer-style-classification-of-piano-sheet","title":"Composer Style Classification of Piano Sheet Music Images Using Language Model Pretraining","date":"2020-07-29","arxiv_id":"2007.14587","n_code_links":1,"syntology":null},{"paper":"/paper/but-fit-at-semeval-2020-task-5-automatic","slug":"but-fit-at-semeval-2020-task-5-automatic","title":"BUT-FIT at SemEval-2020 Task 5: Automatic detection of counterfactual statements with deep pre-trained language representation models","date":"2020-07-28","arxiv_id":"2007.14128","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-brasil-nlp-at-semeval-2020-task","title":"Deep Learning Brasil -- NLP at SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets","date":"2020-07-28","arxiv_id":"2008.01544","n_code_links":0,"syntology":null},{"paper":null,"slug":"guir-at-semeval-2020-task-12-domain-tuned","title":"GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection","date":"2020-07-28","arxiv_id":"2007.14477","n_code_links":0,"syntology":null},{"paper":"/paper/improving-results-on-russian-sentiment","slug":"improving-results-on-russian-sentiment","title":"Improving Results on Russian Sentiment Datasets","date":"2020-07-28","arxiv_id":"2007.14310","n_code_links":1,"syntology":null},{"paper":null,"slug":"tensorcoder-dimension-wise-attention-via","title":"TensorCoder: Dimension-Wise Attention via Tensor Representation for Natural Language Modeling","date":"2020-07-28","arxiv_id":"2008.01547","n_code_links":0,"syntology":null}],"record_sha256":"98eec4537beda9a3787aa561c752fd9043cb00d7e482f5e44c66cd3ab63b137b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}