{"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/absolute-position-encodings/papers/129","list_of":"/method/absolute-position-encodings","method":"Absolute Position Encodings","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":129,"pages_in_order":140,"rows_per_page":100,"rows":[12801,12900],"of":13942,"counts":{"archive_papers_tagged":13942,"with_a_code_link":6505,"where_syntology_ran_a_sample":2224,"not_listed_spam_title":0,"listed":13942,"listed_where_code_ran":2224,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1897,"every_run_a_failure_of_syntologys_instrument":327,"listed_with_a_run_with_no_instrument_failure":1897,"listed_every_run_a_failure_of_syntologys_instrument":327,"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/absolute-position-encodings","prev":"/method/absolute-position-encodings/papers/128","next":"/method/absolute-position-encodings/papers/130","papers":[{"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":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":"/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":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":"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":"/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":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":"/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":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},{"paper":null,"slug":"to-bert-or-not-to-bert-comparing-speech-and","title":"To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection","date":"2020-07-26","arxiv_id":"2008.01551","n_code_links":0,"syntology":null},{"paper":"/paper/fissa-at-semeval-2020-task-9-fine-tuned-for","slug":"fissa-at-semeval-2020-task-9-fine-tuned-for","title":"FiSSA at SemEval-2020 Task 9: Fine-tuned For Feelings","date":"2020-07-24","arxiv_id":"2007.12544","n_code_links":1,"syntology":null},{"paper":"/paper/exploring-swedish-english-fasttext-embeddings","slug":"exploring-swedish-english-fasttext-embeddings","title":"Exploring Swedish & English fastText Embeddings for NER with the Transformer","date":"2020-07-23","arxiv_id":"2007.16007","n_code_links":1,"syntology":null},{"paper":null,"slug":"analogical-reasoning-for-visually-grounded","title":"Analogical Reasoning for Visually Grounded Language Acquisition","date":"2020-07-22","arxiv_id":"2007.11668","n_code_links":0,"syntology":null},{"paper":"/paper/crosstransformers-spatially-aware-few-shot","slug":"crosstransformers-spatially-aware-few-shot","title":"CrossTransformers: spatially-aware few-shot transfer","date":"2020-07-22","arxiv_id":"2007.11498","n_code_links":6,"syntology":{"ran":5,"of":6,"n_ran_checked":5,"n_instrument":0,"unverified":1,"pointer_only":2,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["google-research/meta-dataset"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["community","listed"]}}},{"paper":"/paper/deepsvg-a-hierarchical-generative-network-for","slug":"deepsvg-a-hierarchical-generative-network-for","title":"DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation","date":"2020-07-22","arxiv_id":"2007.11301","n_code_links":2,"syntology":{"ran":5,"of":25,"n_ran_checked":4,"n_instrument":1,"unverified":20,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 20 unverified","official":{"repos":["alexandre01/deepsvg"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":20,"ran_from_kinds":["official"]}}},{"paper":"/paper/neural-machine-translation-with-error","slug":"neural-machine-translation-with-error","title":"Neural Machine Translation with Error Correction","date":"2020-07-21","arxiv_id":"2007.10681","n_code_links":1,"syntology":null},{"paper":null,"slug":"sliceout-training-transformers-and-cnns","title":"Improving compute efficacy frontiers with SliceOut","date":"2020-07-21","arxiv_id":"2007.10909","n_code_links":0,"syntology":null},{"paper":"/paper/conformer-kernel-with-query-term-independence","slug":"conformer-kernel-with-query-term-independence","title":"Conformer-Kernel with Query Term Independence for Document Retrieval","date":"2020-07-20","arxiv_id":"2007.10434","n_code_links":1,"syntology":null},{"paper":"/paper/learning-joint-spatial-temporal","slug":"learning-joint-spatial-temporal","title":"Learning Joint Spatial-Temporal Transformations for Video Inpainting","date":"2020-07-20","arxiv_id":"2007.10247","n_code_links":2,"syntology":{"ran":3,"of":7,"n_ran_checked":2,"n_instrument":1,"unverified":4,"pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["researchmm/STTN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"feature-pyramid-transformer","title":"Feature Pyramid Transformer","date":"2020-07-18","arxiv_id":"2007.09451","n_code_links":0,"syntology":null},{"paper":"/paper/temporal-pointwise-convolutional-networks-for","slug":"temporal-pointwise-convolutional-networks-for","title":"Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit","date":"2020-07-18","arxiv_id":"2007.09483","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-based-traffic-surveillance","title":"Deep Learning Based Traffic Surveillance System For Missing and Suspicious Car Detection","date":"2020-07-17","arxiv_id":"2007.08783","n_code_links":0,"syntology":null},{"paper":"/paper/hopfield-networks-is-all-you-need","slug":"hopfield-networks-is-all-you-need","title":"Hopfield Networks is All You Need","date":"2020-07-16","arxiv_id":"2008.02217","n_code_links":3,"syntology":{"ran":8,"of":9,"n_ran_checked":8,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["ml-jku/hopfield-layers"],"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/infoxlm-an-information-theoretic-framework","slug":"infoxlm-an-information-theoretic-framework","title":"InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training","date":"2020-07-15","arxiv_id":"2007.07834","n_code_links":4,"syntology":null},{"paper":null,"slug":"the-monte-carlo-transformer-a-stochastic-self","title":"The Monte Carlo Transformer: a stochastic self-attention model for sequence prediction","date":"2020-07-15","arxiv_id":"2007.08620","n_code_links":0,"syntology":null},{"paper":"/paper/contextualized-code-representation-learning","slug":"contextualized-code-representation-learning","title":"CoreGen: Contextualized Code Representation Learning for Commit Message Generation","date":"2020-07-14","arxiv_id":"2007.06934","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-transformer-based-data-augmentation-with","title":"Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR","date":"2020-07-14","arxiv_id":"2007.06949","n_code_links":0,"syntology":null},{"paper":"/paper/emoji-prediction-extensions-and-benchmarking","slug":"emoji-prediction-extensions-and-benchmarking","title":"Emoji Prediction: Extensions and Benchmarking","date":"2020-07-14","arxiv_id":"2007.07389","n_code_links":1,"syntology":null},{"paper":"/paper/paranoid-transformer-reading-narrative-of","slug":"paranoid-transformer-reading-narrative-of","title":"Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity","date":"2020-07-13","arxiv_id":"2007.06290","n_code_links":1,"syntology":null},{"paper":null,"slug":"transformer-with-depth-wise-lstm","title":"Rewiring the Transformer with Depth-Wise LSTMs","date":"2020-07-13","arxiv_id":"2007.06257","n_code_links":0,"syntology":null},{"paper":null,"slug":"sparse-graph-to-sequence-learning-for-vision","title":"Sparse Graph to Sequence Learning for Vision Conditioned Long Textual Sequence Generation","date":"2020-07-12","arxiv_id":"2007.06077","n_code_links":0,"syntology":null},{"paper":"/paper/tera-self-supervised-learning-of-transformer","slug":"tera-self-supervised-learning-of-transformer","title":"TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech","date":"2020-07-12","arxiv_id":"2007.06028","n_code_links":7,"syntology":{"ran":11,"of":14,"n_ran_checked":7,"n_instrument":4,"unverified":3,"pointer_only":3,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","official":{"repos":["andi611/Self-Supervised-Speech-Pretraining-and-Representation-Learning","s3prl/s3prl"],"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/sequence-generation-with-mixed","slug":"sequence-generation-with-mixed","title":"Sequence Generation with Mixed Representations","date":"2020-07-11","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/bison-bm25-weighted-self-attention-framework","slug":"bison-bm25-weighted-self-attention-framework","title":"GLOW : Global Weighted Self-Attention Network for Web Search","date":"2020-07-10","arxiv_id":"2007.05186","n_code_links":1,"syntology":null},{"paper":"/paper/advances-of-transformer-based-models-for-news","slug":"advances-of-transformer-based-models-for-news","title":"Advances of Transformer-Based Models for News Headline Generation","date":"2020-07-09","arxiv_id":"2007.05044","n_code_links":2,"syntology":null},{"paper":null,"slug":"deepsinger-singing-voice-synthesis-with-data","title":"DeepSinger: Singing Voice Synthesis with Data Mined From the Web","date":"2020-07-09","arxiv_id":"2007.04590","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatio-temporal-scene-graphs-for-video-dialog","title":"Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers","date":"2020-07-08","arxiv_id":"2007.03848","n_code_links":0,"syntology":null},{"paper":"/paper/do-transformers-need-deep-long-range-memory-1","slug":"do-transformers-need-deep-long-range-memory-1","title":"Do Transformers Need Deep Long-Range Memory","date":"2020-07-07","arxiv_id":"2007.03356","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-go-transformer-natural-language-modeling","title":"The Go Transformer: Natural Language Modeling for Game Play","date":"2020-07-07","arxiv_id":"2007.03500","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-segment-anatomical-structures","title":"Learning to Segment Anatomical Structures Accurately from One Exemplar","date":"2020-07-06","arxiv_id":"2007.03052","n_code_links":0,"syntology":null},{"paper":null,"slug":"relevance-transformer-generating-concise-code","title":"Relevance Transformer: Generating Concise Code Snippets with Relevance Feedback","date":"2020-07-06","arxiv_id":"2007.02609","n_code_links":0,"syntology":null},{"paper":null,"slug":"abstractive-and-mixed-summarization-for-long","title":"Abstractive and mixed summarization for long-single documents","date":"2020-07-03","arxiv_id":"2007.01918","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-transformer-approach-to-contextual-sarcasm","title":"A Transformer Approach to Contextual Sarcasm Detection in Twitter","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptation-of-multilingual-transformer","title":"Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal Dependency Parsing","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"addressing-posterior-collapse-with-mutual","title":"Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"an-empirical-investigation-of-neural-methods","title":"An empirical investigation of neural methods for content scoring of science explanations","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"casia-s-system-for-iwslt-2020-open-domain","title":"CASIA's System for IWSLT 2020 Open Domain Translation","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"character-aware-models-with-similarity","title":"Character aware models with similarity learning for metaphor detection","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"combining-subword-representations-into-word","title":"Combining Subword Representations into Word-level Representations in the Transformer Architecture","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"compressing-neural-machine-translation-models","title":"Compressing Neural Machine Translation Models with 4-bit Precision","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/contextualized-emotion-recognition-in","slug":"contextualized-emotion-recognition-in","title":"Contextualized Emotion Recognition in Conversation as Sequence Tagging","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"copybert-a-unified-approach-to-question","title":"CopyBERT: A Unified Approach to Question Generation with Self-Attention","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"data-augmentation-for-transformer-based-g2p","title":"Data Augmentation for Transformer-based G2P","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-blue-sonics-submission-to-iwslt-2020","title":"Deep Blue Sonics' Submission to IWSLT 2020 Open Domain Translation Task","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"dependency-graph-enhanced-dual-transformer","title":"Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/dialogpt-large-scale-generative-pre-training-1","slug":"dialogpt-large-scale-generative-pre-training-1","title":"DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation","date":"2020-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-and-high-quality-neural-machine","title":"Efficient and High-Quality Neural Machine Translation with OpenNMT","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/end-to-end-offline-speech-translation-system","slug":"end-to-end-offline-speech-translation-system","title":"End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-simultaneous-translation-system","title":"End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-transformer-with-sememe-knowledge","title":"Enhancing Transformer with Sememe Knowledge","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"expand-and-filter-cuni-and-lmu-systems-for","title":"Expand and Filter: CUNI and LMU Systems for the WNGT 2020 Duolingo Shared Task","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"feature-projection-for-improved-text","title":"Feature Projection for Improved Text Classification","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"frustratingly-easy-multilingual-grapheme-to","title":"Frustratingly Easy Multilingual Grapheme-to-Phoneme Conversion","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"generating-medical-reports-from-patient","title":"Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"grapheme-to-phoneme-conversion-with-a","title":"Grapheme-to-Phoneme Conversion with a Multilingual Transformer Model","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/hausamt-v1-0-towards-english-hausa-neural-1","slug":"hausamt-v1-0-towards-english-hausa-neural-1","title":"HausaMT v1.0: Towards English--Hausa Neural Machine Translation","date":"2020-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"how-to-tame-your-data-data-augmentation-for","title":"How to Tame Your Data: Data Augmentation for Dialog State Tracking","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-document-level-neural-machine","title":"Improving Document-Level Neural Machine Translation with Domain Adaptation","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"in-neural-machine-translation-what-does","title":"In Neural Machine Translation, What Does Transfer Learning Transfer?","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"kit-s-iwslt-2020-slt-translation-system","title":"KIT's IWSLT 2020 SLT Translation System","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-principal-parts-for-morphological","title":"Leveraging Principal Parts for Morphological Inflection","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"methods-for-extracting-information-from","title":"Methods for Extracting Information from Messages from Primary Care Providers to Specialists","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-transformer-for-multimodal-machine","slug":"multimodal-transformer-for-multimodal-machine","title":"Multimodal Transformer for Multimodal Machine Translation","date":"2020-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-transduction-of-letter-position","title":"Neural Transduction of Letter Position Dyslexia using an Anagram Matrix Representation","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"oppo-s-machine-translation-system-for-the","title":"OPPO's Machine Translation System for the IWSLT 2020 Open Domain Translation Task","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"paraphrase-generation-by-learning-how-to-edit","title":"Paraphrase Generation by Learning How to Edit from Samples","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-neural-machine-translation-with-asr","title":"Robust Neural Machine Translation with ASR Errors","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"self-attention-guided-copy-mechanism-for","title":"Self-Attention Guided Copy Mechanism for Abstractive Summarization","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"srpol-s-system-for-the-iwslt-2020-end-to-end","title":"SRPOL's System for the IWSLT 2020 End-to-End Speech Translation Task","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"the-afrl-iwslt-2020-systems-work-from-home","title":"The AFRL IWSLT 2020 Systems: Work-From-Home Edition","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"the-hw-tsc-video-speech-translation-system-at","title":"The HW-TSC Video Speech Translation System at IWSLT 2020","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-stream-translation-adaptive","title":"Towards Stream Translation: Adaptive Computation Time for Simultaneous Machine Translation","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"training-and-inference-methods-for-high","title":"Training and Inference Methods for High-Coverage Neural Machine Translation","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"university-of-tsukuba-s-machine-translation","title":"University of Tsukuba's Machine Translation System for IWSLT20 Open Domain Translation Task","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"xiaomi-s-submissions-for-iwslt-2020-open","title":"Xiaomi's Submissions for IWSLT 2020 Open Domain Translation Task","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"berters-multimodal-representation-learning","title":"BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer","date":"2020-06-30","arxiv_id":"2007.07229","n_code_links":0,"syntology":null},{"paper":null,"slug":"correction-of-faulty-background-knowledge","title":"Correction of Faulty Background Knowledge based on Condition Aware and Revise Transformer for Question Answering","date":"2020-06-30","arxiv_id":"2006.16722","n_code_links":0,"syntology":null},{"paper":"/paper/gshard-scaling-giant-models-with-conditional","slug":"gshard-scaling-giant-models-with-conditional","title":"GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding","date":"2020-06-30","arxiv_id":"2006.16668","n_code_links":2,"syntology":{"ran":9,"of":10,"n_ran_checked":4,"n_instrument":5,"unverified":1,"pointer_only":1,"phrase":"9 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/image-level-harmonization-of-multi-site-data","slug":"image-level-harmonization-of-multi-site-data","title":"Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks","date":"2020-06-30","arxiv_id":"2006.16741","n_code_links":1,"syntology":null},{"paper":"/paper/a-transformer-based-joint-encoding-for-1","slug":"a-transformer-based-joint-encoding-for-1","title":"A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis","date":"2020-06-29","arxiv_id":"2006.15955","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"interpreting-hierarchical-linguistic","title":"Building Interpretable Interaction Trees for Deep NLP Models","date":"2020-06-29","arxiv_id":"2007.04298","n_code_links":0,"syntology":null},{"paper":"/paper/multi-head-attention-collaborate-instead-of","slug":"multi-head-attention-collaborate-instead-of","title":"Multi-Head Attention: Collaborate Instead of Concatenate","date":"2020-06-29","arxiv_id":"2006.16362","n_code_links":2,"syntology":null},{"paper":"/paper/predicting-length-of-stay-in-the-intensive","slug":"predicting-length-of-stay-in-the-intensive","title":"Predicting Length of Stay in the Intensive Care Unit with Temporal Pointwise Convolutional Networks","date":"2020-06-29","arxiv_id":"2006.16109","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"4 ran (of which 2 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) · 2 unverified","official":{"repos":["EmmaRocheteau/eICU-LoS-prediction"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["community"]}}},{"paper":"/paper/simplifying-models-with-unlabeled-output-data","slug":"simplifying-models-with-unlabeled-output-data","title":"Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization","date":"2020-06-29","arxiv_id":"2006.16205","n_code_links":2,"syntology":{"ran":14,"of":14,"n_ran_checked":12,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"14 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["p-lambda/composed_finetuning","p-lambda/unlabeled_outputs"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/bottom-up-human-pose-estimation-by-ranking","slug":"bottom-up-human-pose-estimation-by-ranking","title":"Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint Estimates","date":"2020-06-28","arxiv_id":"2006.15480","n_code_links":1,"syntology":null},{"paper":"/paper/rethinking-the-positional-encoding-in","slug":"rethinking-the-positional-encoding-in","title":"Rethinking Positional Encoding in Language Pre-training","date":"2020-06-28","arxiv_id":"2006.15595","n_code_links":3,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["guolinke/TUPE"],"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":["listed","official"]}}},{"paper":null,"slug":"self-attention-networks-for-intent-detection-1","title":"Self-Attention Networks for Intent Detection","date":"2020-06-28","arxiv_id":"2006.15585","n_code_links":0,"syntology":null},{"paper":null,"slug":"mind-the-facts-knowledge-boosted-coherent","title":"Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization","date":"2020-06-27","arxiv_id":"2006.15435","n_code_links":0,"syntology":null}],"record_sha256":"93de978af6580f6690aa823aad4f8edd7e1edaf98e6615e84400be9f34a8945c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}