{"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/dense-connections/papers/277","list_of":"/method/dense-connections","method":"Dense Connections","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":277,"pages_in_order":293,"rows_per_page":100,"rows":[27601,27700],"of":29230,"counts":{"archive_papers_tagged":29230,"with_a_code_link":12972,"where_syntology_ran_a_sample":3929,"not_listed_spam_title":0,"listed":29230,"listed_where_code_ran":3929,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3303,"every_run_a_failure_of_syntologys_instrument":626,"listed_with_a_run_with_no_instrument_failure":3303,"listed_every_run_a_failure_of_syntologys_instrument":626,"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/dense-connections","prev":"/method/dense-connections/papers/276","next":"/method/dense-connections/papers/278","papers":[{"paper":null,"slug":"friendsqa-open-domain-question-answering-on","title":"FriendsQA: Open-Domain Question Answering on TV Show Transcripts","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/incidental-supervision-from-question","slug":"incidental-supervision-from-question","title":"QuASE: Question-Answer Driven Sentence Encoding","date":"2019-09-01","arxiv_id":"1909.00333","n_code_links":1,"syntology":null},{"paper":null,"slug":"multilingual-language-models-for-named-entity","title":"Multilingual Language Models for Named Entity Recognition in German and English","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"multilingual-probing-of-deep-pre-trained","title":"Multilingual Probing of Deep Pre-Trained Contextual Encoders","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/pre-training-of-deep-contextualized","slug":"pre-training-of-deep-contextualized","title":"Global Entity Disambiguation with BERT","date":"2019-09-01","arxiv_id":"1909.00426","n_code_links":1,"syntology":null},{"paper":null,"slug":"predicting-sentiment-of-polish-language-short","title":"Predicting Sentiment of Polish Language Short Texts","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"semantic-role-labeling-with-pretrained","title":"Semantic Role Labeling with Pretrained Language Models for Known and Unknown Predicates","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"to-combine-or-not-to-combine-a-rainbow-deep","title":"To Combine or Not To Combine? A Rainbow Deep Reinforcement Learning Agent for Dialog Policies","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"turkish-tweet-classification-with-transformer","title":"Turkish Tweet Classification with Transformer Encoder","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-learning-with-contextual","title":"Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER","date":"2019-08-31","arxiv_id":"1909.00153","n_code_links":0,"syntology":null},{"paper":"/paper/evaluation-benchmarks-and-learning","slug":"evaluation-benchmarks-and-learning","title":"Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations","date":"2019-08-31","arxiv_id":"1909.00142","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 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) · 0 unverified","official":{"repos":["ZeweiChu/DiscoEval"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/humor-detection-a-transformer-gets-the-last","slug":"humor-detection-a-transformer-gets-the-last","title":"Humor Detection: A Transformer Gets the Last Laugh","date":"2019-08-31","arxiv_id":"1909.00252","n_code_links":2,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["orionw/RedditHumorDetection"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"improving-multi-head-attention-with-capsule","title":"Improving Multi-Head Attention with Capsule Networks","date":"2019-08-31","arxiv_id":"1909.00188","n_code_links":0,"syntology":null},{"paper":null,"slug":"knowledge-enhanced-attention-for-robust","title":"Knowledge Enhanced Attention for Robust Natural Language Inference","date":"2019-08-31","arxiv_id":"1909.00102","n_code_links":0,"syntology":null},{"paper":"/paper/modeling-graph-structure-in-transformer-for","slug":"modeling-graph-structure-in-transformer-for","title":"Modeling Graph Structure in Transformer for Better AMR-to-Text Generation","date":"2019-08-31","arxiv_id":"1909.00136","n_code_links":1,"syntology":null},{"paper":"/paper/nezha-neural-contextualized-representation","slug":"nezha-neural-contextualized-representation","title":"NEZHA: Neural Contextualized Representation for Chinese Language Understanding","date":"2019-08-31","arxiv_id":"1909.00204","n_code_links":10,"syntology":null},{"paper":null,"slug":"quantity-doesnt-buy-quality-syntax-with","title":"Quantity doesn't buy quality syntax with neural language models","date":"2019-08-31","arxiv_id":"1909.00111","n_code_links":0,"syntology":null},{"paper":null,"slug":"small-and-practical-bert-models-for-sequence","title":"Small and Practical BERT Models for Sequence Labeling","date":"2019-08-31","arxiv_id":"1909.00100","n_code_links":0,"syntology":null},{"paper":"/paper/adapt-or-get-left-behind-domain-adaptation","slug":"adapt-or-get-left-behind-domain-adaptation","title":"Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification","date":"2019-08-30","arxiv_id":"1908.11860","n_code_links":3,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["deepopinion/domain-adapted-atsc"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"paper":"/paper/adaptively-sparse-transformers","slug":"adaptively-sparse-transformers","title":"Adaptively Sparse Transformers","date":"2019-08-30","arxiv_id":"1909.00015","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"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) · 0 unverified","official":{"repos":["deep-spin/entmax"],"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":"answering-conversational-questions-on","title":"Answering Conversational Questions on Structured Data without Logical Forms","date":"2019-08-30","arxiv_id":"1908.11787","n_code_links":0,"syntology":null},{"paper":"/paper/bilingual-is-at-least-monolingual-balm-a","slug":"bilingual-is-at-least-monolingual-balm-a","title":"Bilingual is At Least Monolingual (BALM): A Novel Translation Algorithm that Encodes Monolingual Priors","date":"2019-08-30","arxiv_id":"1909.01146","n_code_links":1,"syntology":null},{"paper":"/paper/ebpc-extended-bit-plane-compression-for-deep","slug":"ebpc-extended-bit-plane-compression-for-deep","title":"EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators","date":"2019-08-30","arxiv_id":"1908.11645","n_code_links":2,"syntology":null},{"paper":null,"slug":"learning-rich-representations-for-structured","title":"Learning Rich Representations For Structured Visual Prediction Tasks","date":"2019-08-30","arxiv_id":"1908.11820","n_code_links":0,"syntology":null},{"paper":"/paper/maximizing-mutual-information-for-tacotron","slug":"maximizing-mutual-information-for-tacotron","title":"Maximizing Mutual Information for Tacotron","date":"2019-08-30","arxiv_id":"1909.01145","n_code_links":2,"syntology":null},{"paper":"/paper/multi-modal-fusion-for-end-to-end-rgb-t","slug":"multi-modal-fusion-for-end-to-end-rgb-t","title":"Multi-Modal Fusion for End-to-End RGB-T Tracking","date":"2019-08-30","arxiv_id":"1908.11714","n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-language-model-for-automated","title":"Pre-training A Neural Language Model Improves The Sample Efficiency of an Emergency Room Classification Model","date":"2019-08-30","arxiv_id":"1909.01136","n_code_links":0,"syntology":null},{"paper":"/paper/paws-x-a-cross-lingual-adversarial-dataset","slug":"paws-x-a-cross-lingual-adversarial-dataset","title":"PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification","date":"2019-08-30","arxiv_id":"1908.11828","n_code_links":3,"syntology":null},{"paper":"/paper/transformer-dissection-an-unified","slug":"transformer-dissection-an-unified","title":"Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel","date":"2019-08-30","arxiv_id":"1908.11775","n_code_links":1,"syntology":null},{"paper":"/paper/improving-deep-transformer-with-depth-scaled","slug":"improving-deep-transformer-with-depth-scaled","title":"Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention","date":"2019-08-29","arxiv_id":"1908.11365","n_code_links":1,"syntology":null},{"paper":null,"slug":"probing-representations-learned-by-multimodal","title":"Probing Representations Learned by Multimodal Recurrent and Transformer Models","date":"2019-08-29","arxiv_id":"1908.11125","n_code_links":0,"syntology":null},{"paper":null,"slug":"regularized-context-gates-on-transformer-for","title":"Regularized Context Gates on Transformer for Machine Translation","date":"2019-08-29","arxiv_id":"1908.11020","n_code_links":0,"syntology":null},{"paper":"/paper/temporal-consistency-objectives-regularize","slug":"temporal-consistency-objectives-regularize","title":"Temporal Consistency Objectives Regularize the Learning of Disentangled Representations","date":"2019-08-29","arxiv_id":"1908.11330","n_code_links":1,"syntology":null},{"paper":null,"slug":"adversarial-representation-learning-for-text","title":"Adversarial Representation Learning for Text-to-Image Matching","date":"2019-08-28","arxiv_id":"1908.10534","n_code_links":0,"syntology":null},{"paper":"/paper/inception-inspired-lstm-for-next-frame-video","slug":"inception-inspired-lstm-for-next-frame-video","title":"Inception-inspired LSTM for Next-frame Video Prediction","date":"2019-08-28","arxiv_id":"1909.05622","n_code_links":2,"syntology":null},{"paper":null,"slug":"solving-math-word-problems-with-double","title":"Solving Math Word Problems with Double-Decoder Transformer","date":"2019-08-28","arxiv_id":"1908.10924","n_code_links":0,"syntology":null},{"paper":null,"slug":"bridging-the-gap-for-tokenizer-free-language","title":"Bridging the Gap for Tokenizer-Free Language Models","date":"2019-08-27","arxiv_id":"1908.10322","n_code_links":0,"syntology":null},{"paper":"/paper/finbert-financial-sentiment-analysis-with-pre","slug":"finbert-financial-sentiment-analysis-with-pre","title":"FinBERT: Financial Sentiment Analysis with Pre-trained Language Models","date":"2019-08-27","arxiv_id":"1908.10063","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"multiresolution-transformer-networks","title":"Multiresolution Transformer Networks: Recurrence is Not Essential for Modeling Hierarchical Structure","date":"2019-08-27","arxiv_id":"1908.10408","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-nmt-search-errors-and-model-errors-cat-got","title":"On NMT Search Errors and Model Errors: Cat Got Your Tongue?","date":"2019-08-27","arxiv_id":"1908.10090","n_code_links":0,"syntology":null},{"paper":"/paper/sentence-bert-sentence-embeddings-using","slug":"sentence-bert-sentence-embeddings-using","title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","date":"2019-08-27","arxiv_id":"1908.10084","n_code_links":64,"syntology":{"ran":33,"of":58,"n_ran_checked":30,"n_instrument":3,"unverified":25,"pointer_only":11,"phrase":"33 ran (of which 9 constructed an object rather than computing a result; 30 with no instrument failure: 1 honoured, 0 violated, 29 with no contract checked; 3 where Syntology's instrument failed) · 25 unverified","official":{"repos":["UKPLab/sentence-transformers"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/synthetic-patches-real-images-screening-for","slug":"synthetic-patches-real-images-screening-for","title":"Synthetic patches, real images: screening for centrosome aberrations in EM images of human cancer cells","date":"2019-08-27","arxiv_id":"1908.10109","n_code_links":1,"syntology":null},{"paper":"/paper/attentive-history-selection-for","slug":"attentive-history-selection-for","title":"Attentive History Selection for Conversational Question Answering","date":"2019-08-26","arxiv_id":"1908.09456","n_code_links":2,"syntology":null},{"paper":"/paper/detecting-toxicity-in-news-articles","slug":"detecting-toxicity-in-news-articles","title":"Detecting Toxicity in News Articles: Application to Bulgarian","date":"2019-08-26","arxiv_id":"1908.09785","n_code_links":1,"syntology":null},{"paper":"/paper/does-bert-agree-evaluating-knowledge-of","slug":"does-bert-agree-evaluating-knowledge-of","title":"Does BERT agree? Evaluating knowledge of structure dependence through agreement relations","date":"2019-08-26","arxiv_id":"1908.09892","n_code_links":1,"syntology":null},{"paper":"/paper/gated-convolutional-networks-with-hybrid","slug":"gated-convolutional-networks-with-hybrid","title":"Gated Convolutional Networks with Hybrid Connectivity for Image Classification","date":"2019-08-26","arxiv_id":"1908.09699","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":0,"n_instrument":4,"unverified":1,"pointer_only":5,"phrase":"4 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; 4 where Syntology's instrument failed) · 1 unverified","official":{"repos":["winycg/HCGNet"],"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","unlocated"]}}},{"paper":null,"slug":"measuring-patent-claim-generation-by-span","title":"Measuring Patent Claim Generation by Span Relevancy","date":"2019-08-26","arxiv_id":"1908.09591","n_code_links":0,"syntology":null},{"paper":null,"slug":"see-more-than-once-kernel-sharing-atrous","title":"See More Than Once -- Kernel-Sharing Atrous Convolution for Semantic Segmentation","date":"2019-08-26","arxiv_id":"1908.09443","n_code_links":0,"syntology":null},{"paper":"/paper/patient-knowledge-distillation-for-bert-model","slug":"patient-knowledge-distillation-for-bert-model","title":"Patient Knowledge Distillation for BERT Model Compression","date":"2019-08-25","arxiv_id":"1908.09355","n_code_links":5,"syntology":{"ran":19,"of":27,"n_ran_checked":13,"n_instrument":6,"unverified":8,"pointer_only":27,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 2 honoured, 1 violated, 10 with no contract checked; 6 where Syntology's instrument failed) · 8 unverified","official":{"repos":["intersun/PKD-for-BERT-Model-Compression"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/transforming-delete-retrieve-generate","slug":"transforming-delete-retrieve-generate","title":"Transforming Delete, Retrieve, Generate Approach for Controlled Text Style Transfer","date":"2019-08-25","arxiv_id":"1908.09368","n_code_links":1,"syntology":null},{"paper":"/paper/bert-for-coreference-resolution-baselines-and","slug":"bert-for-coreference-resolution-baselines-and","title":"BERT for Coreference Resolution: Baselines and Analysis","date":"2019-08-24","arxiv_id":"1908.09091","n_code_links":2,"syntology":null},{"paper":null,"slug":"plexus-convolutional-neural-network-plexusnet","title":"Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis","date":"2019-08-24","arxiv_id":"1908.09067","n_code_links":0,"syntology":null},{"paper":null,"slug":"release-strategies-and-the-social-impacts-of","title":"Release Strategies and the Social Impacts of Language Models","date":"2019-08-24","arxiv_id":"1908.09203","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-based-chatbot-models","slug":"deep-learning-based-chatbot-models","title":"Deep Learning Based Chatbot Models","date":"2019-08-23","arxiv_id":"1908.08835","n_code_links":1,"syntology":null},{"paper":null,"slug":"generating-high-resolution-fashion-model","title":"Generating High-Resolution Fashion Model Images Wearing Custom Outfits","date":"2019-08-23","arxiv_id":"1908.08847","n_code_links":0,"syntology":null},{"paper":"/paper/mish-a-self-regularized-non-monotonic-neural","slug":"mish-a-self-regularized-non-monotonic-neural","title":"Mish: A Self Regularized Non-Monotonic Activation Function","date":"2019-08-23","arxiv_id":"1908.08681","n_code_links":9,"syntology":{"ran":9,"of":12,"n_ran_checked":8,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["digantamisra98/Mish"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/neural-data-to-text-generation-a-comparison","slug":"neural-data-to-text-generation-a-comparison","title":"Neural data-to-text generation: A comparison between pipeline and end-to-end architectures","date":"2019-08-23","arxiv_id":"1908.09022","n_code_links":1,"syntology":null},{"paper":null,"slug":"training-optimus-prime-md-generating-medical","title":"Training Optimus Prime, M.D.: Generating Medical Certification Items by Fine-Tuning OpenAI's gpt2 Transformer Model","date":"2019-08-23","arxiv_id":"1908.08594","n_code_links":0,"syntology":null},{"paper":"/paper/well-read-students-learn-better-the-impact-of","slug":"well-read-students-learn-better-the-impact-of","title":"Well-Read Students Learn Better: On the Importance of Pre-training Compact Models","date":"2019-08-23","arxiv_id":"1908.08962","n_code_links":40,"syntology":{"ran":21,"of":32,"n_ran_checked":16,"n_instrument":5,"unverified":11,"pointer_only":4,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 1 honoured, 0 violated, 15 with no contract checked; 5 where Syntology's instrument failed) · 11 unverified","official":{"repos":["google-research/bert"],"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":"denoising-based-sequence-to-sequence-pre","title":"Denoising based Sequence-to-Sequence Pre-training for Text Generation","date":"2019-08-22","arxiv_id":"1908.08206","n_code_links":0,"syntology":null},{"paper":null,"slug":"feedbackward-decoding-for-semantic","title":"Feedbackward Decoding for Semantic Segmentation","date":"2019-08-22","arxiv_id":"1908.08584","n_code_links":0,"syntology":null},{"paper":"/paper/multi-passage-bert-a-globally-normalized-bert","slug":"multi-passage-bert-a-globally-normalized-bert","title":"Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering","date":"2019-08-22","arxiv_id":"1908.08167","n_code_links":0,"syntology":null},{"paper":"/paper/revisit-semantic-representation-and-tree","slug":"revisit-semantic-representation-and-tree","title":"Revisiting Semantic Representation and Tree Search for Similar Question Retrieval","date":"2019-08-22","arxiv_id":"1908.08326","n_code_links":1,"syntology":null},{"paper":"/paper/text-summarization-with-pretrained-encoders","slug":"text-summarization-with-pretrained-encoders","title":"Text Summarization with Pretrained Encoders","date":"2019-08-22","arxiv_id":"1908.08345","n_code_links":19,"syntology":{"ran":13,"of":21,"n_ran_checked":12,"n_instrument":1,"unverified":8,"pointer_only":5,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 1 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","official":{"repos":["nlpyang/PreSumm"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/the-compositionality-of-neural-networks","slug":"the-compositionality-of-neural-networks","title":"Compositionality decomposed: how do neural networks generalise?","date":"2019-08-22","arxiv_id":"1908.08351","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":["i-machine-think/am-i-compositional"],"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/vl-bert-pre-training-of-generic-visual","slug":"vl-bert-pre-training-of-generic-visual","title":"VL-BERT: Pre-training of Generic Visual-Linguistic Representations","date":"2019-08-22","arxiv_id":"1908.08530","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jackroos/VL-BERT"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"190807688","title":"Improving Neural Machine Translation with Pre-trained Representation","date":"2019-08-21","arxiv_id":"1908.07688","n_code_links":0,"syntology":null},{"paper":"/paper/190807721","slug":"190807721","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","date":"2019-08-21","arxiv_id":"1908.07721","n_code_links":1,"syntology":null},{"paper":null,"slug":"revealing-the-dark-secrets-of-bert","title":"Revealing the Dark Secrets of BERT","date":"2019-08-21","arxiv_id":"1908.08593","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-contextualized-embeddings-on-54","slug":"evaluating-contextualized-embeddings-on-54","title":"Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing","date":"2019-08-20","arxiv_id":"1908.07448","n_code_links":0,"syntology":null},{"paper":"/paper/glossbert-bert-for-word-sense-disambiguation","slug":"glossbert-bert-for-word-sense-disambiguation","title":"GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge","date":"2019-08-20","arxiv_id":"1908.07245","n_code_links":3,"syntology":null},{"paper":"/paper/image-synthesis-from-reconfigurable-layout","slug":"image-synthesis-from-reconfigurable-layout","title":"Image Synthesis From Reconfigurable Layout and Style","date":"2019-08-20","arxiv_id":"1908.07500","n_code_links":4,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["iVMCL/LostGANs"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/lxmert-learning-cross-modality-encoder","slug":"lxmert-learning-cross-modality-encoder","title":"LXMERT: Learning Cross-Modality Encoder Representations from Transformers","date":"2019-08-20","arxiv_id":"1908.07490","n_code_links":9,"syntology":{"ran":4,"of":15,"n_ran_checked":4,"n_instrument":0,"unverified":11,"pointer_only":3,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 11 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","official":{"repos":["airsplay/lxmert"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/pix2pose-pixel-wise-coordinate-regression-of","slug":"pix2pose-pixel-wise-coordinate-regression-of","title":"Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation","date":"2019-08-20","arxiv_id":"1908.07433","n_code_links":3,"syntology":{"ran":4,"of":8,"n_ran_checked":4,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":null}},{"paper":"/paper/tabnet-attentive-interpretable-tabular","slug":"tabnet-attentive-interpretable-tabular","title":"TabNet: Attentive Interpretable Tabular Learning","date":"2019-08-20","arxiv_id":"1908.07442","n_code_links":19,"syntology":{"ran":13,"of":17,"n_ran_checked":12,"n_instrument":1,"unverified":4,"pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["google-research/google-research"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/universal-adversarial-triggers-for-nlp","slug":"universal-adversarial-triggers-for-nlp","title":"Universal Adversarial Triggers for Attacking and Analyzing NLP","date":"2019-08-20","arxiv_id":"1908.07125","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"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) · 0 unverified","official":{"repos":["Eric-Wallace/universal-triggers"],"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":["official"]}}},{"paper":null,"slug":"a-study-of-bert-for-non-factoid-question","title":"A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints","date":"2019-08-19","arxiv_id":"1908.06780","n_code_links":0,"syntology":null},{"paper":"/paper/align-mask-and-select-a-simple-method-for","slug":"align-mask-and-select-a-simple-method-for","title":"Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models","date":"2019-08-19","arxiv_id":"1908.06725","n_code_links":0,"syntology":null},{"paper":"/paper/deep-active-lesion-segmentation","slug":"deep-active-lesion-segmentation","title":"Deep Active Lesion Segmentation","date":"2019-08-19","arxiv_id":"1908.06933","n_code_links":1,"syntology":null},{"paper":"/paper/encoder-agnostic-adaptation-for-conditional","slug":"encoder-agnostic-adaptation-for-conditional","title":"Encoder-Agnostic Adaptation for Conditional Language Generation","date":"2019-08-19","arxiv_id":"1908.06938","n_code_links":1,"syntology":null},{"paper":"/paper/neural-architectures-for-nested-ner-through-1","slug":"neural-architectures-for-nested-ner-through-1","title":"Neural Architectures for Nested NER through Linearization","date":"2019-08-19","arxiv_id":"1908.06926","n_code_links":1,"syntology":null},{"paper":"/paper/panet-few-shot-image-semantic-segmentation","slug":"panet-few-shot-image-semantic-segmentation","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","date":"2019-08-18","arxiv_id":"1908.06391","n_code_links":5,"syntology":null},{"paper":"/paper/vusfavariational-universal-successor-features","slug":"vusfavariational-universal-successor-features","title":"VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation","date":"2019-08-18","arxiv_id":"1908.06376","n_code_links":2,"syntology":null},{"paper":"/paper/emotionx-idea-emotion-bert-an-affectional","slug":"emotionx-idea-emotion-bert-an-affectional","title":"EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation","date":"2019-08-17","arxiv_id":"1908.06264","n_code_links":1,"syntology":null},{"paper":null,"slug":"hard-but-robust-easy-but-sensitive-how","title":"Hard but Robust, Easy but Sensitive: How Encoder and Decoder Perform in Neural Machine Translation","date":"2019-08-17","arxiv_id":"1908.06259","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-features-matter-effective-language","title":"Language Features Matter: Effective Language Representations for Vision-Language Tasks","date":"2019-08-17","arxiv_id":"1908.06327","n_code_links":0,"syntology":null},{"paper":"/paper/bert-based-multi-head-selection-for-joint","slug":"bert-based-multi-head-selection-for-joint","title":"BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction","date":"2019-08-16","arxiv_id":"1908.05908","n_code_links":1,"syntology":null},{"paper":null,"slug":"cfo-a-framework-for-building-production-nlp","title":"CFO: A Framework for Building Production NLP Systems","date":"2019-08-16","arxiv_id":"1908.06121","n_code_links":0,"syntology":null},{"paper":"/paper/clutrr-a-diagnostic-benchmark-for-inductive","slug":"clutrr-a-diagnostic-benchmark-for-inductive","title":"CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text","date":"2019-08-16","arxiv_id":"1908.06177","n_code_links":5,"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":["facebookresearch/clutrr"],"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":"iterative-update-and-unified-representation","title":"Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning","date":"2019-08-16","arxiv_id":"1908.06758","n_code_links":0,"syntology":null},{"paper":"/paper/scarletnas-bridging-the-gap-between","slug":"scarletnas-bridging-the-gap-between","title":"SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search","date":"2019-08-16","arxiv_id":"1908.06022","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-transference-architecture-for-automatic","title":"The Transference Architecture for Automatic Post-Editing","date":"2019-08-16","arxiv_id":"1908.06151","n_code_links":0,"syntology":null},{"paper":"/paper/unicoder-vl-a-universal-encoder-for-vision","slug":"unicoder-vl-a-universal-encoder-for-vision","title":"Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training","date":"2019-08-16","arxiv_id":"1908.06066","n_code_links":0,"syntology":null},{"paper":null,"slug":"accelerated-cnn-training-through-gradient","title":"Accelerated CNN Training Through Gradient Approximation","date":"2019-08-15","arxiv_id":"1908.05460","n_code_links":0,"syntology":null},{"paper":"/paper/m-bert-injecting-multimodal-information-in","slug":"m-bert-injecting-multimodal-information-in","title":"Integrating Multimodal Information in Large Pretrained Transformers","date":"2019-08-15","arxiv_id":"1908.05787","n_code_links":1,"syntology":null},{"paper":"/paper/temporal-collaborative-ranking-via","slug":"temporal-collaborative-ranking-via","title":"Temporal Collaborative Ranking Via Personalized Transformer","date":"2019-08-15","arxiv_id":"1908.05435","n_code_links":3,"syntology":null},{"paper":"/paper/towards-making-the-most-of-bert-in-neural","slug":"towards-making-the-most-of-bert-in-neural","title":"Towards Making the Most of BERT in Neural Machine Translation","date":"2019-08-15","arxiv_id":"1908.05672","n_code_links":2,"syntology":null},{"paper":null,"slug":"transformer-based-automatic-post-editing-with","title":"Transformer-based Automatic Post-Editing with a Context-Aware Encoding Approach for Multi-Source Inputs","date":"2019-08-15","arxiv_id":"1908.05679","n_code_links":0,"syntology":null},{"paper":null,"slug":"visualizing-and-understanding-the","title":"Visualizing and Understanding the Effectiveness of BERT","date":"2019-08-15","arxiv_id":"1908.05620","n_code_links":0,"syntology":null},{"paper":null,"slug":"adabot-fault-tolerant-java-decompiler","title":"Adabot: Fault-Tolerant Java Decompiler","date":"2019-08-14","arxiv_id":"1908.06748","n_code_links":0,"syntology":null}],"record_sha256":"1635883cda4a7135ab2cb1bc64b27b815c5622e656f5faf47fbed95bc7102ac9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}