{"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/translation/papers/98","list_of":"/task/translation","task":"Translation","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":98,"pages_in_order":124,"rows_per_page":100,"rows":[9701,9800],"of":12395,"counts":{"archive_papers_tagged":12395,"with_a_code_link":3574,"where_syntology_ran_a_sample":682,"not_listed_spam_title":0,"listed":12395,"listed_where_code_ran":682,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":566,"every_run_a_failure_of_syntologys_instrument":116,"listed_with_a_run_with_no_instrument_failure":566,"listed_every_run_a_failure_of_syntologys_instrument":116,"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/translation","prev":"/task/translation/papers/97","next":"/task/translation/papers/99","papers":[{"url":null,"slug":"neural-semantic-parsing","title":"Neural Semantic Parsing","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"news-2018-whitepaper","title":"NEWS 2018 Whitepaper","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nict-self-training-approach-to-neural-machine","title":"NICT Self-Training Approach to Neural Machine Translation at NMT-2018","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"opennmt-system-description-for-wnmt-2018-800","title":"OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"openseq2seq-extensible-toolkit-for","title":"OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"proceedings-of-the-2nd-workshop-on-neural","title":"Proceedings of the 2nd Workshop on Neural Machine Translation and Generation","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-features-for-strong-performance-on","title":"Simple Features for Strong Performance on Named Entity Recognition in Code-Switched Twitter Data","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"supernmt-neural-machine-translation-with","title":"SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"texar-a-modularized-versatile-and-extensible","title":"Texar: A Modularized, Versatile, and Extensible Toolbox for Text Generation","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-summa-platform-a-scalable-infrastructure","title":"The SUMMA Platform: A Scalable Infrastructure for Multi-lingual Multi-media Monitoring","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"translating-a-language-you-donat-know-in-the","title":"Translating a Language You Don't Know In the Chinese Room","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transliteration-better-than-translation","title":"Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-source-hierarchies-for-low","title":"Unsupervised Source Hierarchies for Low-Resource Neural Machine Translation","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"word-embedding-and-wordnet-based-metaphor","title":"Word Embedding and WordNet Based Metaphor Identification and Interpretation","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-machine-translation-with-key-value","title":"Neural Machine Translation with Key-Value Memory-Augmented Attention","date":"2018-06-29","arxiv_id":"1806.11249","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-mapping-image-to-image-translation-with","title":"Multi-Mapping Image-to-Image Translation with Central Biasing Normalization","date":"2018-06-26","arxiv_id":"1806.10050","repositories_listed":0,"syntology":null},{"url":null,"slug":"ir2vi-enhanced-night-environmental-perception","title":"IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation","date":"2018-06-25","arxiv_id":"1806.09565","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-machine-translation-for-low-resource","title":"Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora","date":"2018-06-25","arxiv_id":"1806.09652","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-optimal-transport-with-global","title":"Towards Optimal Transport with Global Invariances","date":"2018-06-25","arxiv_id":"1806.09277","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-learning-across-domains-with","title":"Variational learning across domains with triplet information","date":"2018-06-22","arxiv_id":"1806.08672","repositories_listed":0,"syntology":null},{"url":null,"slug":"bfgan-backward-and-forward-generative","title":"BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation","date":"2018-06-21","arxiv_id":"1806.08097","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-chunk-based-feedback-in-neural","title":"Learning from Chunk-based Feedback in Neural Machine Translation","date":"2018-06-19","arxiv_id":"1806.07169","repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-analysis-of-image-to-image","title":"Theoretical Analysis of Image-to-Image Translation with Adversarial Learning","date":"2018-06-19","arxiv_id":"1806.07001","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-transformer-and-recurrent","title":"A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation","date":"2018-06-18","arxiv_id":"1806.06957","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-robustness-of-radiomic-features-by","title":"Assessing robustness of radiomic features by image perturbation","date":"2018-06-18","arxiv_id":"1806.06719","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-word-segmentation-from-speech","title":"Unsupervised Word Segmentation from Speech with Attention","date":"2018-06-18","arxiv_id":"1806.06734","repositories_listed":0,"syntology":null},{"url":null,"slug":"medgan-medical-image-translation-using-gans","title":"MedGAN: Medical Image Translation using GANs","date":"2018-06-17","arxiv_id":"1806.06397","repositories_listed":0,"syntology":null},{"url":null,"slug":"show-attend-and-translate-unsupervised-image","title":"Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention","date":"2018-06-16","arxiv_id":"1806.06195","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-toybox-dataset-of-egocentric-visual","title":"The Toybox Dataset of Egocentric Visual Object Transformations","date":"2018-06-15","arxiv_id":"1806.06034","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-neural-machine-translation","title":"Generative Neural Machine Translation","date":"2018-06-13","arxiv_id":"1806.05138","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-and-generalizing-back-translation","title":"Explaining and Generalizing Back-Translation through Wake-Sleep","date":"2018-06-12","arxiv_id":"1806.04402","repositories_listed":0,"syntology":null},{"url":null,"slug":"fusing-recency-into-neural-machine","title":"Fusing Recency into Neural Machine Translation with an Inter-Sentence Gate Model","date":"2018-06-12","arxiv_id":"1806.04466","repositories_listed":0,"syntology":null},{"url":null,"slug":"navigating-with-graph-representations-for","title":"Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models","date":"2018-06-11","arxiv_id":"1806.04189","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-decoding-and-training-methods-for","title":"Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation","date":"2018-06-10","arxiv_id":"1806.03661","repositories_listed":0,"syntology":null},{"url":null,"slug":"findings-of-the-second-workshop-on-neural","title":"Findings of the Second Workshop on Neural Machine Translation and Generation","date":"2018-06-08","arxiv_id":"1806.02940","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-neural-machine-translation-with","title":"Multilingual Neural Machine Translation with Task-Specific Attention","date":"2018-06-08","arxiv_id":"1806.03280","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-challenge-set-for-french-english-machine","title":"A Challenge Set for French --> English Machine Translation","date":"2018-06-07","arxiv_id":"1806.02725","repositories_listed":0,"syntology":null},{"url":null,"slug":"characterizing-departures-from-linearity-in","title":"Characterizing Departures from Linearity in Word Translation","date":"2018-06-07","arxiv_id":"1806.04508","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-source-neural-machine-translation-with","title":"Multi-Source Neural Machine Translation with Missing Data","date":"2018-06-07","arxiv_id":"1806.02525","repositories_listed":0,"syntology":null},{"url":null,"slug":"pointflownet-learning-representations-for","title":"PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds","date":"2018-06-06","arxiv_id":"1806.02170","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-do-source-side-monolingual-word","title":"How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?","date":"2018-06-05","arxiv_id":"1806.01515","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-data-generation-for-end-to-end","title":"Synthetic data generation for end-to-end thermal infrared tracking","date":"2018-06-04","arxiv_id":"1806.01013","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-locality-sensitive-hashing-for-beam","title":"Fast Locality Sensitive Hashing for Beam Search on GPU","date":"2018-06-02","arxiv_id":"1806.00588","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-two-paraphrase-models-for","title":"A Comparison of Two Paraphrase Models for Taxonomy Augmentation","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multimodal-translation-based-approach-for","title":"A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-domain-adaptation-for-neural","title":"A Survey of Domain Adaptation for Neural Machine Translation","date":"2018-06-01","arxiv_id":"1806.00258","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-paraphrase-lattice-creation-for","title":"Automated Paraphrase Lattice Creation for HyTER Machine Translation Evaluation","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-character-and-word-information-in","title":"Combining Character and Word Information in Neural Machine Translation Using a Multi-Level Attention","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"da-gan-instance-level-image-translation-by-1","title":"DA-GAN: Instance-Level Image Translation by Deep Attention Generative Adversarial Networks","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-sequence-learning-with-group","title":"Efficient Sequence Learning with Group Recurrent Networks","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"from-dictations-to-clinical-reports-using","title":"From dictations to clinical reports using machine translation","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-bridging-network-for-neural","title":"Generative Bridging Network for Neural Sequence Prediction","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-hidden-unit-contribution-for","title":"Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lexical-conceptual-structure-of-literal-and","title":"Lexical Conceptual Structure of Literal and Metaphorical Spatial Language: A Case Study of ``Push''","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meaningless-yet-meaningful-morphology","title":"Meaningless yet meaningful: Morphology grounded subword-level NMT","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mesoscopic-facial-geometry-inference-using","title":"Mesoscopic Facial Geometry Inference Using Deep Neural Networks","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"morphological-word-embeddings-for-arabic","title":"Morphological Word Embeddings for Arabic Neural Machine Translation in Low-Resource Settings","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-module-recurrent-neural-networks-with","title":"Multi-Module Recurrent Neural Networks with Transfer Learning","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-poetry-translation","title":"Neural Poetry Translation","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"noising-and-denoising-natural-language","title":"Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"quality-estimation-for-automatically","title":"Quality Estimation for Automatically Generated Titles of eCommerce Browse Pages","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"quickedit-editing-text-translations-by-1","title":"QuickEdit: Editing Text \\& Translations by Crossing Words Out","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"target-foresight-based-attention-for-neural","title":"Target Foresight Based Attention for Neural Machine Translation","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-word-vectors-to-improve-word-alignments","title":"Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vis-eval-metric-viewer-a-visualisation-tool","title":"Vis-Eval Metric Viewer: A Visualisation Tool for Inspecting and Evaluating Metric Scores of Machine Translation Output","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"anaphora-and-coreference-resolution-a-review","title":"Anaphora and Coreference Resolution: A Review","date":"2018-05-30","arxiv_id":"1805.11824","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-cascade-attention-based-rnn-for","title":"Context-aware Cascade Attention-based RNN for Video Emotion Recognition","date":"2018-05-30","arxiv_id":"1805.12098","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-using-domain-randomization","title":"Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images","date":"2018-05-30","arxiv_id":"1805.11778","repositories_listed":0,"syntology":null},{"url":null,"slug":"bi-directional-neural-machine-translation","title":"Bi-Directional Neural Machine Translation with Synthetic Parallel Data","date":"2018-05-29","arxiv_id":"1805.11213","repositories_listed":0,"syntology":null},{"url":null,"slug":"cerfgan-a-compact-effective-robust-and-fast","title":"CerfGAN: A Compact, Effective, Robust, and Fast Model for Unsupervised Multi-Domain Image-to-Image Translation","date":"2018-05-28","arxiv_id":"1805.10871","repositories_listed":0,"syntology":null},{"url":null,"slug":"exemplar-guided-unsupervised-image-to-image","title":"Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency","date":"2018-05-28","arxiv_id":"1805.11145","repositories_listed":0,"syntology":null},{"url":null,"slug":"inducing-grammars-with-and-for-neural-machine","title":"Inducing Grammars with and for Neural Machine Translation","date":"2018-05-28","arxiv_id":"1805.10850","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometric-understanding-of-deep-learning","title":"Geometric Understanding of Deep Learning","date":"2018-05-26","arxiv_id":"1805.10451","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-neural-machine-translation","title":"Context-Aware Neural Machine Translation Learns Anaphora Resolution","date":"2018-05-25","arxiv_id":"1805.10163","repositories_listed":0,"syntology":null},{"url":null,"slug":"japanese-predicate-conjugation-for-neural","title":"Japanese Predicate Conjugation for Neural Machine Translation","date":"2018-05-25","arxiv_id":"1805.10047","repositories_listed":0,"syntology":null},{"url":null,"slug":"pathology-segmentation-using-distributional","title":"Pathology Segmentation using Distributional Differences to Images of Healthy Origin","date":"2018-05-25","arxiv_id":"1805.10344","repositories_listed":0,"syntology":null},{"url":null,"slug":"phrase-table-as-recommendation-memory-for","title":"Phrase Table as Recommendation Memory for Neural Machine Translation","date":"2018-05-25","arxiv_id":"1805.09960","repositories_listed":0,"syntology":null},{"url":null,"slug":"recursive-neural-network-based-preordering","title":"Recursive Neural Network Based Preordering for English-to-Japanese Machine Translation","date":"2018-05-25","arxiv_id":"1805.10187","repositories_listed":0,"syntology":null},{"url":null,"slug":"refining-source-representations-with-relation-1","title":"Refining Source Representations with Relation Networks for Neural Machine Translation","date":"2018-05-25","arxiv_id":"1805.11154","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervisedly-training-gans-for-segmenting","title":"Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations","date":"2018-05-25","arxiv_id":"1805.10059","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-neural-machine-translation","title":"Fast Neural Machine Translation Implementation","date":"2018-05-24","arxiv_id":"1805.09863","repositories_listed":0,"syntology":null},{"url":null,"slug":"filtering-and-mining-parallel-data-in-a-joint","title":"Filtering and Mining Parallel Data in a Joint Multilingual Space","date":"2018-05-24","arxiv_id":"1805.09822","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperbolic-attention-networks","title":"Hyperbolic Attention Networks","date":"2018-05-24","arxiv_id":"1805.09786","repositories_listed":0,"syntology":null},{"url":null,"slug":"implicit-autoencoders","title":"Implicit Autoencoders","date":"2018-05-24","arxiv_id":"1805.09804","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-modeling-teaches-you-more-than-1","title":"Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Task Analysis","date":"2018-05-24","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-parallel-texts-of-books-translations-in","title":"The parallel texts of books translations in the quality evaluation of basic models and algorithms for the similarity of symbol strings","date":"2018-05-24","arxiv_id":"1805.09776","repositories_listed":0,"syntology":null},{"url":null,"slug":"discrete-structural-planning-for-neural-1","title":"Discrete Structural Planning for Neural Machine Translation","date":"2018-05-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-much-does-a-word-weigh-weighting-word","title":"How much does a word weigh? Weighting word embeddings for word sense induction","date":"2018-05-23","arxiv_id":"1805.09209","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-for-low-resource-neural-machine-1","title":"Meta-Learning for Low-Resource Neural Machine Translation","date":"2018-05-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"selecting-machine-translated-data-for-quick","title":"Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System","date":"2018-05-23","arxiv_id":"1805.09119","repositories_listed":0,"syntology":null},{"url":null,"slug":"spherical-convolutional-neural-network-for-3d","title":"Spherical Convolutional Neural Network for 3D Point Clouds","date":"2018-05-21","arxiv_id":"1805.07872","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-localization-and-motion-transfer","title":"Object Localization with a Weakly Supervised CapsNet","date":"2018-05-20","arxiv_id":"1805.07706","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-un-parallel-corpus-annotated-for","title":"The UN Parallel Corpus Annotated for Translation Direction","date":"2018-05-20","arxiv_id":"1805.07697","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-repair-software-vulnerabilities","title":"Learning to Repair Software Vulnerabilities with Generative Adversarial Networks","date":"2018-05-18","arxiv_id":"1805.07475","repositories_listed":0,"syntology":null},{"url":null,"slug":"metric-for-automatic-machine-translation","title":"Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations","date":"2018-05-18","arxiv_id":"1805.07469","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-cross-modal-alignment-of-speech","title":"Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces","date":"2018-05-18","arxiv_id":"1805.07467","repositories_listed":0,"syntology":null},{"url":null,"slug":"xogan-one-to-many-unsupervised-image-to-image","title":"XOGAN: One-to-Many Unsupervised Image-to-Image Translation","date":"2018-05-18","arxiv_id":"1805.07277","repositories_listed":0,"syntology":null},{"url":null,"slug":"translation-of-algorithmic-descriptions-of","title":"Translation of Algorithmic Descriptions of Discrete Functions to SAT with Applications to Cryptanalysis Problems","date":"2018-05-17","arxiv_id":"1805.07239","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-bleu-and-meaning-representation-in","title":"Are BLEU and Meaning Representation in Opposition?","date":"2018-05-16","arxiv_id":"1805.06536","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-relatedness-for-all-languages-a","title":"Semantic Relatedness for All (Languages): A Comparative Analysis of Multilingual Semantic Relatedness Using Machine Translation","date":"2018-05-16","arxiv_id":"1805.06522","repositories_listed":0,"syntology":null}],"record_sha256":"b903bd8226e90b4126f48ccf607ce60bd643e2788355a811f3b69856d1e4566f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}