{"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":"/task/retrieval/papers/46","list_of":"/task/retrieval","task":"Retrieval","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":46,"pages_in_order":143,"rows_per_page":100,"rows":[4501,4600],"of":14297,"counts":{"archive_papers_tagged":14297,"with_a_code_link":5274,"where_syntology_ran_a_sample":1303,"not_listed_spam_title":0,"listed":14297,"listed_where_code_ran":1303,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1067,"every_run_a_failure_of_syntologys_instrument":236,"listed_with_a_run_with_no_instrument_failure":1067,"listed_every_run_a_failure_of_syntologys_instrument":236,"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":"/task/retrieval","prev":"/task/retrieval/papers/45","next":"/task/retrieval/papers/47","papers":[{"url":"/paper/forkgan-seeing-into-the-rainy-night","slug":"forkgan-seeing-into-the-rainy-night","title":"ForkGAN: Seeing into the Rainy Night","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-acoustic-images-for-effective-self","slug":"leveraging-acoustic-images-for-effective-self","title":"Leveraging Acoustic Images for Effective Self-Supervised Audio Representation Learning","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ultra-light-deep-mir-by-trimming-lottery","slug":"ultra-light-deep-mir-by-trimming-lottery","title":"Ultra-light deep MIR by trimming lottery tickets","date":"2020-07-31","arxiv_id":"2007.16187","repositories_listed":1,"syntology":null},{"url":"/paper/neuralqa-a-usable-library-for-question","slug":"neuralqa-a-usable-library-for-question","title":"NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets","date":"2020-07-30","arxiv_id":"2007.15211","repositories_listed":1,"syntology":null},{"url":"/paper/cross-modal-hierarchical-modelling-for-fine","slug":"cross-modal-hierarchical-modelling-for-fine","title":"Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval","date":"2020-07-29","arxiv_id":"2007.15103","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-character-graph-via-online-face","slug":"dynamic-character-graph-via-online-face","title":"Dynamic Character Graph via Online Face Clustering for Movie Analysis","date":"2020-07-29","arxiv_id":"2007.14913","repositories_listed":1,"syntology":null},{"url":"/paper/solving-phase-retrieval-with-a-learned","slug":"solving-phase-retrieval-with-a-learned","title":"Solving Phase Retrieval with a Learned Reference","date":"2020-07-29","arxiv_id":"2007.14621","repositories_listed":1,"syntology":null},{"url":"/paper/identity-guided-human-semantic-parsing-for","slug":"identity-guided-human-semantic-parsing-for","title":"Identity-Guided Human Semantic Parsing for Person Re-Identification","date":"2020-07-27","arxiv_id":"2007.13467","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"6 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/identity-guided-human-semantic-parsing-for#ran","syntology_url":"https://syntology.ai/paper/2007.13467","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.13467"}},"official":{"repos":["CASIA-IVA-Lab/ISP-reID"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/receptive-field-regularized-cnns-for-music","slug":"receptive-field-regularized-cnns-for-music","title":"Receptive-Field Regularized CNNs for Music Classification and Tagging","date":"2020-07-27","arxiv_id":"2007.13503","repositories_listed":1,"syntology":null},{"url":"/paper/hard-negative-examples-are-hard-but-useful","slug":"hard-negative-examples-are-hard-but-useful","title":"Hard negative examples are hard, but useful","date":"2020-07-24","arxiv_id":"2007.12749","repositories_listed":1,"syntology":null},{"url":"/paper/ir-bert-leveraging-bert-for-semantic-search","slug":"ir-bert-leveraging-bert-for-semantic-search","title":"IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News Articles","date":"2020-07-24","arxiv_id":"2007.12603","repositories_listed":1,"syntology":null},{"url":"/paper/positive-semidefinite-matrix-factorization-a","slug":"positive-semidefinite-matrix-factorization-a","title":"Positive Semidefinite Matrix Factorization: A Connection with Phase Retrieval and Affine Rank Minimization","date":"2020-07-24","arxiv_id":"2007.12364","repositories_listed":1,"syntology":null},{"url":"/paper/cad-deform-deformable-fitting-of-cad-models","slug":"cad-deform-deformable-fitting-of-cad-models","title":"CAD-Deform: Deformable Fitting of CAD Models to 3D Scans","date":"2020-07-23","arxiv_id":"2007.11965","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_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) · 3 unverified","sample_list":"/paper/cad-deform-deformable-fitting-of-cad-models#ran","syntology_url":"https://syntology.ai/paper/2007.11965","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.11965"}},"official":{"repos":["alexeybokhovkin/CAD-Deform"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/zscrgan-a-gan-based-expectation-maximization","slug":"zscrgan-a-gan-based-expectation-maximization","title":"ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions","date":"2020-07-23","arxiv_id":"2007.12212","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-shape-and-pose-disentanglement","slug":"unsupervised-shape-and-pose-disentanglement","title":"Unsupervised Shape and Pose Disentanglement for 3D Meshes","date":"2020-07-22","arxiv_id":"2007.11341","repositories_listed":1,"syntology":null},{"url":"/paper/check-square-at-checkthat-2020-claim","slug":"check-square-at-checkthat-2020-claim","title":"Check_square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features","date":"2020-07-21","arxiv_id":"2007.10534","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-image-captioning-with-global","slug":"fine-grained-image-captioning-with-global","title":"Fine-Grained Image Captioning with Global-Local Discriminative Objective","date":"2020-07-21","arxiv_id":"2007.10662","repositories_listed":1,"syntology":null},{"url":"/paper/multi-modal-transformer-for-video-retrieval","slug":"multi-modal-transformer-for-video-retrieval","title":"Multi-modal Transformer for Video Retrieval","date":"2020-07-21","arxiv_id":"2007.10639","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-modal-transformer-for-video-retrieval#ran","syntology_url":"https://syntology.ai/paper/2007.10639","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.10639"}},"official":null}},{"url":"/paper/time-frequency-scattering-accurately-models","slug":"time-frequency-scattering-accurately-models","title":"Time-Frequency Scattering Accurately Models Auditory Similarities Between Instrumental Playing Techniques","date":"2020-07-21","arxiv_id":"2007.10926","repositories_listed":1,"syntology":null},{"url":"/paper/a-comparison-of-supervised-learning-to-match","slug":"a-comparison-of-supervised-learning-to-match","title":"A Comparison of Supervised Learning to Match Methods for Product Search","date":"2020-07-20","arxiv_id":"2007.10296","repositories_listed":1,"syntology":null},{"url":"/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","repositories_listed":1,"syntology":null},{"url":"/paper/consensus-aware-visual-semantic-embedding-for","slug":"consensus-aware-visual-semantic-embedding-for","title":"Consensus-Aware Visual-Semantic Embedding for Image-Text Matching","date":"2020-07-17","arxiv_id":"2007.08883","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/consensus-aware-visual-semantic-embedding-for#ran","syntology_url":"https://syntology.ai/paper/2007.08883","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.08883"}},"official":{"repos":["BruceW91/CVSE"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/dh3d-deep-hierarchical-3d-descriptors-for","slug":"dh3d-deep-hierarchical-3d-descriptors-for","title":"DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization","date":"2020-07-17","arxiv_id":"2007.09217","repositories_listed":1,"syntology":null},{"url":"/paper/are-we-there-yet-evaluating-state-of-the-art","slug":"are-we-there-yet-evaluating-state-of-the-art","title":"Are We There Yet? Evaluating State-of-the-Art Neural Network based Geoparsers Using EUPEG as a Benchmarking Platform","date":"2020-07-15","arxiv_id":"2007.07455","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-image-retrieval","slug":"conditional-image-retrieval","title":"MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval","date":"2020-07-14","arxiv_id":"2007.07177","repositories_listed":1,"syntology":null},{"url":"/paper/deep-retrieval-an-end-to-end-learnable","slug":"deep-retrieval-an-end-to-end-learnable","title":"Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations","date":"2020-07-12","arxiv_id":"2007.07203","repositories_listed":1,"syntology":null},{"url":"/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","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-many-way-few-shot-video","slug":"generalized-many-way-few-shot-video","title":"Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation","date":"2020-07-09","arxiv_id":"2007.04755","repositories_listed":1,"syntology":null},{"url":"/paper/less-is-more-rejecting-unreliable-reviews-for","slug":"less-is-more-rejecting-unreliable-reviews-for","title":"Less is More: Rejecting Unreliable Reviews for Product Question Answering","date":"2020-07-09","arxiv_id":"2007.04526","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-and-interpreting-caption","slug":"evaluating-and-interpreting-caption","title":"Evaluating and interpreting caption prediction for histopathology images","date":"2020-07-08","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/on-learning-semantic-representations-for","slug":"on-learning-semantic-representations-for","title":"On Learning Semantic Representations for Million-Scale Free-Hand Sketches","date":"2020-07-07","arxiv_id":"2007.04101","repositories_listed":1,"syntology":null},{"url":"/paper/a-retrieve-and-rewrite-initialization-method","slug":"a-retrieve-and-rewrite-initialization-method","title":"A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/clireval-evaluating-machine-translation-as-a","slug":"clireval-evaluating-machine-translation-as-a","title":"CLIReval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fused-text-recogniser-and-deep-embeddings","slug":"fused-text-recogniser-and-deep-embeddings","title":"Fused Text Recogniser and Deep Embeddings Improve Word Recognition and Retrieval","date":"2020-07-01","arxiv_id":"2007.00166","repositories_listed":1,"syntology":null},{"url":"/paper/machine-reading-of-historical-events","slug":"machine-reading-of-historical-events","title":"Machine Reading of Historical Events","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-context-aware-covid-19","slug":"self-supervised-context-aware-covid-19","title":"Self-supervised context-aware COVID-19 document exploration through atlas grounding","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/answering-questions-on-covid-19-in-real-time","slug":"answering-questions-on-covid-19-in-real-time","title":"Answering Questions on COVID-19 in Real-Time","date":"2020-06-29","arxiv_id":"2006.15830","repositories_listed":1,"syntology":null},{"url":"/paper/learning-sparse-prototypes-for-text","slug":"learning-sparse-prototypes-for-text","title":"Learning Sparse Prototypes for Text Generation","date":"2020-06-29","arxiv_id":"2006.16336","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":3,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"8 ran (of which 3 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","sample_list":"/paper/learning-sparse-prototypes-for-text#ran","syntology_url":"https://syntology.ai/paper/2006.16336","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.16336"}},"official":{"repos":["jxhe/sparse-text-prototype"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":3,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-optimal-tree-models-under-beam","slug":"learning-optimal-tree-models-under-beam","title":"Learning Optimal Tree Models Under Beam Search","date":"2020-06-27","arxiv_id":"2006.15408","repositories_listed":1,"syntology":{"n":9,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/learning-optimal-tree-models-under-beam#ran","syntology_url":"https://syntology.ai/paper/2006.15408","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.15408"}},"official":null}},{"url":"/paper/graph-optimal-transport-for-cross-domain","slug":"graph-optimal-transport-for-cross-domain","title":"Graph Optimal Transport for Cross-Domain Alignment","date":"2020-06-26","arxiv_id":"2006.14744","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":2,"n_no_contract":6,"n_pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 2 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/graph-optimal-transport-for-cross-domain#ran","syntology_url":"https://syntology.ai/paper/2006.14744","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.14744"}},"official":{"repos":["LiqunChen0606/Graph-Optimal-Transport"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/robot-object-retrieval-with-contextual","slug":"robot-object-retrieval-with-contextual","title":"Robot Object Retrieval with Contextual Natural Language Queries","date":"2020-06-23","arxiv_id":"2006.13253","repositories_listed":1,"syntology":null},{"url":"/paper/video-playback-rate-perception-for-self-1","slug":"video-playback-rate-perception-for-self-1","title":"Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning","date":"2020-06-20","arxiv_id":"2006.11476","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/video-playback-rate-perception-for-self-1#ran","syntology_url":"https://syntology.ai/paper/2006.11476","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.11476"}},"official":{"repos":["yuanyao366/PRP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/compositional-learning-of-image-text-query","slug":"compositional-learning-of-image-text-query","title":"Compositional Learning of Image-Text Query for Image Retrieval","date":"2020-06-19","arxiv_id":"2006.11149","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_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","sample_list":"/paper/compositional-learning-of-image-text-query#ran","syntology_url":"https://syntology.ai/paper/2006.11149","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.11149"}},"official":{"repos":["ecom-research/ComposeAE"],"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"]}}},{"url":"/paper/neural-topic-modeling-with-continual-lifelong","slug":"neural-topic-modeling-with-continual-lifelong","title":"Neural Topic Modeling with Continual Lifelong Learning","date":"2020-06-19","arxiv_id":"2006.10909","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-and-discourse-topic-aware-neural","slug":"explainable-and-discourse-topic-aware-neural","title":"Explainable and Discourse Topic-aware Neural Language Understanding","date":"2020-06-18","arxiv_id":"2006.10632","repositories_listed":1,"syntology":null},{"url":"/paper/generative-patch-priors-for-practical","slug":"generative-patch-priors-for-practical","title":"Generative Patch Priors for Practical Compressive Image Recovery","date":"2020-06-18","arxiv_id":"2006.10873","repositories_listed":1,"syntology":null},{"url":"/paper/hynet-local-descriptor-with-hybrid-similarity","slug":"hynet-local-descriptor-with-hybrid-similarity","title":"HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss","date":"2020-06-17","arxiv_id":"2006.10202","repositories_listed":1,"syntology":null},{"url":"/paper/avlnet-learning-audio-visual-language","slug":"avlnet-learning-audio-visual-language","title":"AVLnet: Learning Audio-Visual Language Representations from Instructional Videos","date":"2020-06-16","arxiv_id":"2006.09199","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-retrieval-for-iterative-self","slug":"cross-lingual-retrieval-for-iterative-self","title":"Cross-lingual Retrieval for Iterative Self-Supervised Training","date":"2020-06-16","arxiv_id":"2006.09526","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-3d-nanoscale-coherent-imaging-via","slug":"real-time-3d-nanoscale-coherent-imaging-via","title":"Real-time 3D Nanoscale Coherent Imaging via Physics-aware Deep Learning","date":"2020-06-16","arxiv_id":"2006.09441","repositories_listed":1,"syntology":null},{"url":"/paper/dual-level-semantic-transfer-deep-hashing-for","slug":"dual-level-semantic-transfer-deep-hashing-for","title":"Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval","date":"2020-06-10","arxiv_id":"2006.05586","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-relational-reasoning-for","slug":"self-supervised-relational-reasoning-for","title":"Self-Supervised Relational Reasoning for Representation Learning","date":"2020-06-10","arxiv_id":"2006.05849","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/self-supervised-relational-reasoning-for#ran","syntology_url":"https://syntology.ai/paper/2006.05849","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.05849"}},"official":{"repos":["mpatacchiola/self-supervised-relational-reasoning"],"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":["official"]}}},{"url":"/paper/automatic-code-summarization-via-multi","slug":"automatic-code-summarization-via-multi","title":"Retrieval-Augmented Generation for Code Summarization via Hybrid GNN","date":"2020-06-09","arxiv_id":"2006.05405","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-generative-conversational-query","slug":"few-shot-generative-conversational-query","title":"Few-Shot Generative Conversational Query Rewriting","date":"2020-06-09","arxiv_id":"2006.05009","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"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","sample_list":"/paper/few-shot-generative-conversational-query#ran","syntology_url":"https://syntology.ai/paper/2006.05009","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.05009"}},"official":{"repos":["thunlp/ConversationQueryRewriter"],"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","unlocated"]}}},{"url":"/paper/neural-methods-for-point-wise-dependency","slug":"neural-methods-for-point-wise-dependency","title":"Neural Methods for Point-wise Dependency Estimation","date":"2020-06-09","arxiv_id":"2006.05553","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":2,"n_no_contract":9,"n_pointer_only":3,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 2 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/neural-methods-for-point-wise-dependency#ran","syntology_url":"https://syntology.ai/paper/2006.05553","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.05553"}},"official":{"repos":["yaohungt/Pointwise_Dependency_Neural_Estimation"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/phase-retrieval-in-high-dimensions","slug":"phase-retrieval-in-high-dimensions","title":"Phase retrieval in high dimensions: Statistical and computational phase transitions","date":"2020-06-09","arxiv_id":"2006.05228","repositories_listed":1,"syntology":null},{"url":"/paper/m3p-learning-universal-representations-via","slug":"m3p-learning-universal-representations-via","title":"M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training","date":"2020-06-04","arxiv_id":"2006.02635","repositories_listed":1,"syntology":null},{"url":"/paper/rel-an-entity-linker-standing-on-the","slug":"rel-an-entity-linker-standing-on-the","title":"REL: An Entity Linker Standing on the Shoulders of Giants","date":"2020-06-02","arxiv_id":"2006.01969","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_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) · 1 unverified","sample_list":"/paper/rel-an-entity-linker-standing-on-the#ran","syntology_url":"https://syntology.ai/paper/2006.01969","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.01969"}},"official":null}},{"url":"/paper/evade-deep-image-retrieval-by-stashing","slug":"evade-deep-image-retrieval-by-stashing","title":"Evade Deep Image Retrieval by Stashing Private Images in the Hash Space","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/google-landmarks-dataset-v2-a-large-scale-1","slug":"google-landmarks-dataset-v2-a-large-scale-1","title":"Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/screencast-tutorial-video-understanding","slug":"screencast-tutorial-video-understanding","title":"Screencast Tutorial Video Understanding","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sketch-less-for-more-on-the-fly-fine-grained-1","slug":"sketch-less-for-more-on-the-fly-fine-grained-1","title":"Sketch Less for More: On-the-Fly Fine-Grained Sketch-Based Image Retrieval","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-deep-shape-descriptor-with-point","slug":"unsupervised-deep-shape-descriptor-with-point","title":"Unsupervised Deep Shape Descriptor With Point Distribution Learning","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/video-playback-rate-perception-for-self","slug":"video-playback-rate-perception-for-self","title":"Video Playback Rate Perception for Self-Supervised Spatio-Temporal Representation Learning","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/view-gcn-view-based-graph-convolutional","slug":"view-gcn-view-based-graph-convolutional","title":"View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/automatic-generation-of-topic-labels","slug":"automatic-generation-of-topic-labels","title":"Automatic Generation of Topic Labels","date":"2020-05-29","arxiv_id":"2006.00127","repositories_listed":1,"syntology":null},{"url":"/paper/user-behavior-retrieval-for-click-through","slug":"user-behavior-retrieval-for-click-through","title":"User Behavior Retrieval for Click-Through Rate Prediction","date":"2020-05-28","arxiv_id":"2005.14171","repositories_listed":1,"syntology":null},{"url":"/paper/narmada-need-and-available-resource-managing","slug":"narmada-need-and-available-resource-managing","title":"NARMADA: Need and Available Resource Managing Assistant for Disasters and Adversities","date":"2020-05-27","arxiv_id":"2005.13524","repositories_listed":1,"syntology":null},{"url":"/paper/query-resolution-for-conversational-search","slug":"query-resolution-for-conversational-search","title":"Query Resolution for Conversational Search with Limited Supervision","date":"2020-05-24","arxiv_id":"2005.11723","repositories_listed":1,"syntology":null},{"url":"/paper/improving-segmentation-for-technical-support","slug":"improving-segmentation-for-technical-support","title":"Improving Segmentation for Technical Support Problems","date":"2020-05-22","arxiv_id":"2005.11055","repositories_listed":1,"syntology":null},{"url":"/paper/open-retrieval-conversational-question","slug":"open-retrieval-conversational-question","title":"Open-Retrieval Conversational Question Answering","date":"2020-05-22","arxiv_id":"2005.11364","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/open-retrieval-conversational-question#ran","syntology_url":"https://syntology.ai/paper/2005.11364","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.11364"}},"official":{"repos":["prdwb/orconvqa-release"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/accounting-for-input-noise-in-gaussian","slug":"accounting-for-input-noise-in-gaussian","title":"Accounting for Input Noise in Gaussian Process Parameter Retrieval","date":"2020-05-20","arxiv_id":"2005.09907","repositories_listed":1,"syntology":null},{"url":"/paper/comparing-transformers-and-rnns-on-predicting","slug":"comparing-transformers-and-rnns-on-predicting","title":"Human Sentence Processing: Recurrence or Attention?","date":"2020-05-19","arxiv_id":"2005.09471","repositories_listed":1,"syntology":null},{"url":"/paper/sketch-bert-learning-sketch-bidirectional","slug":"sketch-bert-learning-sketch-bidirectional","title":"Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt","date":"2020-05-19","arxiv_id":"2005.09159","repositories_listed":1,"syntology":null},{"url":"/paper/table-search-using-a-deep-contextualized","slug":"table-search-using-a-deep-contextualized","title":"Table Search Using a Deep Contextualized Language Model","date":"2020-05-19","arxiv_id":"2005.09207","repositories_listed":1,"syntology":null},{"url":"/paper/policy-aware-unbiased-learning-to-rank-for","slug":"policy-aware-unbiased-learning-to-rank-for","title":"Policy-Aware Unbiased Learning to Rank for Top-k Rankings","date":"2020-05-18","arxiv_id":"2005.09035","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-low-resource-set-to-description","slug":"cross-lingual-low-resource-set-to-description","title":"Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce","date":"2020-05-17","arxiv_id":"2005.08188","repositories_listed":1,"syntology":null},{"url":"/paper/vpr-bench-an-open-source-visual-place","slug":"vpr-bench-an-open-source-visual-place","title":"VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change","date":"2020-05-17","arxiv_id":"2005.08135","repositories_listed":1,"syntology":{"n":14,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_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) · 9 unverified","sample_list":"/paper/vpr-bench-an-open-source-visual-place#ran","syntology_url":"https://syntology.ai/paper/2005.08135","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.08135"}},"official":{"repos":["MubarizZaffar/VPR-Bench"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-mesh-fine-grained-semantic-indexing-of","slug":"beyond-mesh-fine-grained-semantic-indexing-of","title":"Beyond MeSH: Fine-Grained Semantic Indexing of Biomedical Literature based on Weak Supervision","date":"2020-05-15","arxiv_id":"2005.07638","repositories_listed":1,"syntology":null},{"url":"/paper/detection-and-retrieval-of-out-of","slug":"detection-and-retrieval-of-out-of","title":"Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation","date":"2020-05-14","arxiv_id":"2005.06831","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_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) · 1 unverified","sample_list":"/paper/detection-and-retrieval-of-out-of#ran","syntology_url":"https://syntology.ai/paper/2005.06831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.06831"}},"official":{"repos":["RonMcKay/OODRetrieval"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/local-self-attention-over-long-text-for","slug":"local-self-attention-over-long-text-for","title":"Local Self-Attention over Long Text for Efficient Document Retrieval","date":"2020-05-11","arxiv_id":"2005.04908","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_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) · 3 unverified","sample_list":"/paper/local-self-attention-over-long-text-for#ran","syntology_url":"https://syntology.ai/paper/2005.04908","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.04908"}},"official":{"repos":["sebastian-hofstaetter/transformer-kernel-ranking"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/condensed-movies-story-based-retrieval-with","slug":"condensed-movies-story-based-retrieval-with","title":"Condensed Movies: Story Based Retrieval with Contextual Embeddings","date":"2020-05-08","arxiv_id":"2005.04208","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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","sample_list":"/paper/condensed-movies-story-based-retrieval-with#ran","syntology_url":"https://syntology.ai/paper/2005.04208","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.04208"}},"official":null}},{"url":"/paper/a-tale-of-two-perplexities-sensitivity-of","slug":"a-tale-of-two-perplexities-sensitivity-of","title":"A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer's Type","date":"2020-05-07","arxiv_id":"2005.03593","repositories_listed":1,"syntology":null},{"url":"/paper/cobra-contrastive-bi-modal-representation","slug":"cobra-contrastive-bi-modal-representation","title":"COBRA: Contrastive Bi-Modal Representation Algorithm","date":"2020-05-07","arxiv_id":"2005.03687","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_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) · 2 unverified","sample_list":"/paper/cobra-contrastive-bi-modal-representation#ran","syntology_url":"https://syntology.ai/paper/2005.03687","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.03687"}},"official":{"repos":["ovshake/cobra"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-embedding-empowered-entity-retrieval","slug":"graph-embedding-empowered-entity-retrieval","title":"Graph-Embedding Empowered Entity Retrieval","date":"2020-05-06","arxiv_id":"2005.02843","repositories_listed":1,"syntology":null},{"url":"/paper/multireqa-a-cross-domain-evaluation-for","slug":"multireqa-a-cross-domain-evaluation-for","title":"MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models","date":"2020-05-05","arxiv_id":"2005.02507","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-alignment-based-iterative","slug":"unsupervised-alignment-based-iterative","title":"Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering","date":"2020-05-04","arxiv_id":"2005.01218","repositories_listed":1,"syntology":null},{"url":"/paper/tailoring-and-evaluating-the-wikipedia-for-in","slug":"tailoring-and-evaluating-the-wikipedia-for-in","title":"Tailoring and Evaluating the Wikipedia for in-Domain Comparable Corpora Extraction","date":"2020-05-03","arxiv_id":"2005.01177","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-morphological-paradigm","slug":"unsupervised-morphological-paradigm","title":"Unsupervised Morphological Paradigm Completion","date":"2020-05-03","arxiv_id":"2005.00970","repositories_listed":1,"syntology":null},{"url":"/paper/bert-knn-adding-a-knn-search-component-to","slug":"bert-knn-adding-a-knn-search-component-to","title":"BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA","date":"2020-05-02","arxiv_id":"2005.00766","repositories_listed":1,"syntology":null},{"url":"/paper/pyretri-a-pytorch-based-library-for","slug":"pyretri-a-pytorch-based-library-for","title":"PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks","date":"2020-05-02","arxiv_id":"2005.02154","repositories_listed":1,"syntology":null},{"url":"/paper/do-neural-ranking-models-intensify-gender","slug":"do-neural-ranking-models-intensify-gender","title":"Do Neural Ranking Models Intensify Gender Bias?","date":"2020-05-01","arxiv_id":"2005.00372","repositories_listed":1,"syntology":null},{"url":"/paper/sibert-enhanced-chinese-pre-trained-language","slug":"sibert-enhanced-chinese-pre-trained-language","title":"SiBert: Enhanced Chinese Pre-trained Language Model with Sentence Insertion","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sparse-dense-and-attentional-representations","slug":"sparse-dense-and-attentional-representations","title":"Sparse, Dense, and Attentional Representations for Text Retrieval","date":"2020-05-01","arxiv_id":"2005.00181","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-recurrent-survival-model-for-unbiased","slug":"a-deep-recurrent-survival-model-for-unbiased","title":"A Deep Recurrent Survival Model for Unbiased Ranking","date":"2020-04-30","arxiv_id":"2004.14714","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_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) · 1 unverified","sample_list":"/paper/a-deep-recurrent-survival-model-for-unbiased#ran","syntology_url":"https://syntology.ai/paper/2004.14714","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.14714"}},"official":{"repos":["Jinjiarui/DRSR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/filtering-before-iteratively-referring-for","slug":"filtering-before-iteratively-referring-for","title":"Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots","date":"2020-04-30","arxiv_id":"2004.14550","repositories_listed":1,"syntology":null},{"url":"/paper/progressively-pretrained-dense-corpus-index","slug":"progressively-pretrained-dense-corpus-index","title":"Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering","date":"2020-04-30","arxiv_id":"2005.00038","repositories_listed":1,"syntology":null},{"url":"/paper/text-segmentation-by-cross-segment-attention","slug":"text-segmentation-by-cross-segment-attention","title":"Text Segmentation by Cross Segment Attention","date":"2020-04-30","arxiv_id":"2004.14535","repositories_listed":1,"syntology":null},{"url":"/paper/expansion-via-prediction-of-importance-with","slug":"expansion-via-prediction-of-importance-with","title":"Expansion via Prediction of Importance with Contextualization","date":"2020-04-29","arxiv_id":"2004.14245","repositories_listed":1,"syntology":null},{"url":"/paper/conversational-word-embedding-for-retrieval","slug":"conversational-word-embedding-for-retrieval","title":"Conversational Word Embedding for Retrieval-Based Dialog System","date":"2020-04-28","arxiv_id":"2004.13249","repositories_listed":1,"syntology":null}],"record_sha256":"94aa407037bb4caf1fc35268a41d38e1ab40c72ee4145964e397cb31d7955f7d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}