{"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/lstm/papers/38","list_of":"/method/lstm","method":"LSTM","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":38,"pages_in_order":55,"rows_per_page":100,"rows":[3701,3800],"of":5448,"counts":{"archive_papers_tagged":5448,"with_a_code_link":1823,"where_syntology_ran_a_sample":335,"not_listed_spam_title":0,"listed":5448,"listed_where_code_ran":335,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":283,"every_run_a_failure_of_syntologys_instrument":52,"listed_with_a_run_with_no_instrument_failure":283,"listed_every_run_a_failure_of_syntologys_instrument":52,"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/lstm","prev":"/method/lstm/papers/37","next":"/method/lstm/papers/39","papers":[{"paper":null,"slug":"resource-optimized-neural-architecture-search","title":"Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation","date":"2019-09-02","arxiv_id":"1909.00548","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-phrase-based-statistical-machine","title":"Enhancing Phrase-Based Statistical Machine Translation by Learning Phrase Representations Using Long Short-Term Memory Network","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"eoann-lexical-semantic-relation","title":"EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluation-of-stacked-embeddings-for","title":"Evaluation of Stacked Embeddings for Bulgarian on the Downstream Tasks POS and NERC","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluation-of-vector-embedding-models-in","title":"Evaluation of vector embedding models in clustering of text documents","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-embeddings-for-frame-identification","title":"Graph Embeddings for Frame Identification","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"multilingual-probing-of-deep-pre-trained","title":"Multilingual Probing of Deep Pre-Trained Contextual Encoders","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/neural-architecture-search-for-joint","slug":"neural-architecture-search-for-joint","title":"Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge","date":"2019-09-01","arxiv_id":"1909.00337","n_code_links":1,"syntology":null},{"paper":"/paper/neural-feature-extraction-for-contextual","slug":"neural-feature-extraction-for-contextual","title":"Neural Feature Extraction for Contextual Emotion Detection","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"opinions-summarization-aspect-similarity","title":"Opinions Summarization: Aspect Similarity Recognition Relaxes The Constraint of Predefined Aspects","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"persistence-pays-off-paying-attention-to-what-1","title":"Persistence pays off: Paying Attention to What the LSTM Gating Mechanism Persists","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-sentiment-of-polish-language-short","title":"Predicting Sentiment of Polish Language Short Texts","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"quasi-bidirectional-encoder-representations","title":"Quasi Bidirectional Encoder Representations from Transformers for Word Sense Disambiguation","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"scalable-reinforcement-learning-based-neural","title":"Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research","date":"2019-09-01","arxiv_id":"1909.00311","n_code_links":0,"syntology":null},{"paper":null,"slug":"semantic-role-labeling-with-pretrained","title":"Semantic Role Labeling with Pretrained Language Models for Known and Unknown Predicates","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/sparse-victory-a-large-scale-systematic","slug":"sparse-victory-a-large-scale-systematic","title":"Sparse Victory -- A Large Scale Systematic Comparison of count-based and prediction-based vectorizers for text classification","date":"2019-09-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"speculation-and-negation-detection-in-french","title":"Speculation and Negation detection in French biomedical corpora","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/taskmaster-1-toward-a-realistic-and-diverse","slug":"taskmaster-1-toward-a-realistic-and-diverse","title":"Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset","date":"2019-09-01","arxiv_id":"1909.05358","n_code_links":1,"syntology":null},{"paper":null,"slug":"adversarial-learning-with-contextual","title":"Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER","date":"2019-08-31","arxiv_id":"1909.00153","n_code_links":0,"syntology":null},{"paper":"/paper/evaluation-benchmarks-and-learning","slug":"evaluation-benchmarks-and-learning","title":"Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations","date":"2019-08-31","arxiv_id":"1909.00142","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ZeweiChu/DiscoEval"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"hm-nas-efficient-neural-architecture-search","title":"HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking","date":"2019-08-31","arxiv_id":"1909.00122","n_code_links":0,"syntology":null},{"paper":"/paper/modeling-graph-structure-in-transformer-for","slug":"modeling-graph-structure-in-transformer-for","title":"Modeling Graph Structure in Transformer for Better AMR-to-Text Generation","date":"2019-08-31","arxiv_id":"1909.00136","n_code_links":1,"syntology":null},{"paper":null,"slug":"question-type-driven-question-generation","title":"Question-type Driven Question Generation","date":"2019-08-31","arxiv_id":"1909.00140","n_code_links":0,"syntology":null},{"paper":"/paper/hierarchical-pointer-net-parsing","slug":"hierarchical-pointer-net-parsing","title":"Hierarchical Pointer Net Parsing","date":"2019-08-30","arxiv_id":"1908.11571","n_code_links":1,"syntology":null},{"paper":null,"slug":"sequential-learning-of-convolutional-features","title":"Sequential Learning of Convolutional Features for Effective Text Classification","date":"2019-08-30","arxiv_id":"1909.00080","n_code_links":0,"syntology":null},{"paper":"/paper/remedying-bilstm-cnn-deficiency-in-modeling","slug":"remedying-bilstm-cnn-deficiency-in-modeling","title":"Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER","date":"2019-08-29","arxiv_id":"1908.11046","n_code_links":3,"syntology":null},{"paper":"/paper/scientific-statement-classification-over","slug":"scientific-statement-classification-over","title":"Scientific Statement Classification over arXiv.org","date":"2019-08-29","arxiv_id":"1908.10993","n_code_links":6,"syntology":null},{"paper":null,"slug":"shallow-syntax-in-deep-water","title":"Shallow Syntax in Deep Water","date":"2019-08-29","arxiv_id":"1908.11047","n_code_links":0,"syntology":null},{"paper":"/paper/zero-shot-text-to-sql-learning-with-auxiliary","slug":"zero-shot-text-to-sql-learning-with-auxiliary","title":"Zero-shot Text-to-SQL Learning with Auxiliary Task","date":"2019-08-29","arxiv_id":"1908.11052","n_code_links":1,"syntology":null},{"paper":null,"slug":"analyzing-customer-feedback-for-product-fit","title":"Analyzing Customer Feedback for Product Fit Prediction","date":"2019-08-28","arxiv_id":"1908.10896","n_code_links":0,"syntology":null},{"paper":"/paper/inception-inspired-lstm-for-next-frame-video","slug":"inception-inspired-lstm-for-next-frame-video","title":"Inception-inspired LSTM for Next-frame Video Prediction","date":"2019-08-28","arxiv_id":"1909.05622","n_code_links":2,"syntology":null},{"paper":"/paper/intelligent-active-queue-management-using","slug":"intelligent-active-queue-management-using","title":"Intelligent Active Queue Management Using Explicit Congestion Notification","date":"2019-08-28","arxiv_id":"1909.08386","n_code_links":1,"syntology":null},{"paper":null,"slug":"onto-word-segmentation-of-the-complete-tang","title":"Onto Word Segmentation of the Complete Tang Poems","date":"2019-08-28","arxiv_id":"1908.10621","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-effectiveness-of-low-rank-matrix","title":"On the Effectiveness of Low-Rank Matrix Factorization for LSTM Model Compression","date":"2019-08-27","arxiv_id":"1908.09982","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-emotional-analysis-of-false-information-in","title":"An Emotional Analysis of False Information in Social Media and News Articles","date":"2019-08-26","arxiv_id":"1908.09951","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-toxicity-in-news-articles","slug":"detecting-toxicity-in-news-articles","title":"Detecting Toxicity in News Articles: Application to Bulgarian","date":"2019-08-26","arxiv_id":"1908.09785","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-bounds-of-function-approximations","title":"On the Bounds of Function Approximations","date":"2019-08-26","arxiv_id":"1908.09942","n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-attribute-representation-and","slug":"rethinking-attribute-representation-and","title":"Rethinking Attribute Representation and Injection for Sentiment Classification","date":"2019-08-26","arxiv_id":"1908.09590","n_code_links":1,"syntology":null},{"paper":null,"slug":"test-an-end-to-end-network-traffic","title":"TEST: an End-to-End Network Traffic Examination and Identification Framework Based on Spatio-Temporal Features Extraction","date":"2019-08-26","arxiv_id":"1908.10271","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-domain-adaptation-for-machine","title":"Adversarial Domain Adaptation for Machine Reading Comprehension","date":"2019-08-24","arxiv_id":"1908.09209","n_code_links":0,"syntology":null},{"paper":"/paper/bert-for-coreference-resolution-baselines-and","slug":"bert-for-coreference-resolution-baselines-and","title":"BERT for Coreference Resolution: Baselines and Analysis","date":"2019-08-24","arxiv_id":"1908.09091","n_code_links":2,"syntology":null},{"paper":"/paper/generator-evaluator-selector-net-a-modular","slug":"generator-evaluator-selector-net-a-modular","title":"Generator evaluator-selector net for panoptic image segmentation and splitting unfamiliar objects into parts","date":"2019-08-24","arxiv_id":"1908.09108","n_code_links":2,"syntology":null},{"paper":"/paper/190807724","slug":"190807724","title":"Restricted Recurrent Neural Networks","date":"2019-08-21","arxiv_id":"1908.07724","n_code_links":1,"syntology":{"ran":5,"of":9,"n_ran_checked":5,"n_instrument":0,"unverified":4,"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) · 4 unverified","official":{"repos":["diaoenmao/Restricted-Recurrent-Neural-Networks"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"efficient-deep-neural-networks","title":"Efficient Deep Neural Networks","date":"2019-08-20","arxiv_id":"1908.08926","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-contextualized-embeddings-on-54","slug":"evaluating-contextualized-embeddings-on-54","title":"Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing","date":"2019-08-20","arxiv_id":"1908.07448","n_code_links":0,"syntology":null},{"paper":"/paper/neural-architectures-for-nested-ner-through-1","slug":"neural-architectures-for-nested-ner-through-1","title":"Neural Architectures for Nested NER through Linearization","date":"2019-08-19","arxiv_id":"1908.06926","n_code_links":1,"syntology":null},{"paper":null,"slug":"language-features-matter-effective-language","title":"Language Features Matter: Effective Language Representations for Vision-Language Tasks","date":"2019-08-17","arxiv_id":"1908.06327","n_code_links":0,"syntology":null},{"paper":null,"slug":"structural-health-monitoring-of-cantilever","title":"Structural Health Monitoring of Cantilever Beam, a Case Study -- Using Bayesian Neural Network AND Deep Learning","date":"2019-08-17","arxiv_id":"1908.06326","n_code_links":0,"syntology":null},{"paper":"/paper/boah-a-tool-suite-for-multi-fidelity-bayesian","slug":"boah-a-tool-suite-for-multi-fidelity-bayesian","title":"BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters","date":"2019-08-16","arxiv_id":"1908.06756","n_code_links":1,"syntology":null},{"paper":"/paper/performing-deep-recurrent-double-q-learning","slug":"performing-deep-recurrent-double-q-learning","title":"Performing Deep Recurrent Double Q-Learning for Atari Games","date":"2019-08-16","arxiv_id":"1908.06040","n_code_links":2,"syntology":null},{"paper":"/paper/scarletnas-bridging-the-gap-between","slug":"scarletnas-bridging-the-gap-between","title":"SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search","date":"2019-08-16","arxiv_id":"1908.06022","n_code_links":1,"syntology":null},{"paper":"/paper/conditional-lstm-gan-for-melody-generation","slug":"conditional-lstm-gan-for-melody-generation","title":"Conditional LSTM-GAN for Melody Generation from Lyrics","date":"2019-08-15","arxiv_id":"1908.05551","n_code_links":2,"syntology":null},{"paper":"/paper/scalable-attentive-sentence-pair-modeling-via","slug":"scalable-attentive-sentence-pair-modeling-via","title":"Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding","date":"2019-08-14","arxiv_id":"1908.05161","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-deep-evolutionary-approach-to-bioinspired","title":"A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction","date":"2019-08-13","arxiv_id":"1908.04784","n_code_links":0,"syntology":null},{"paper":"/paper/bioflair-pretrained-pooled-contextualized","slug":"bioflair-pretrained-pooled-contextualized","title":"BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling Tasks","date":"2019-08-13","arxiv_id":"1908.05760","n_code_links":1,"syntology":null},{"paper":"/paper/generative-question-refinement-with-deep","slug":"generative-question-refinement-with-deep","title":"Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System","date":"2019-08-13","arxiv_id":"1908.05604","n_code_links":1,"syntology":null},{"paper":"/paper/lstm-vs-gru-vs-bidirectional-rnn-for-script","slug":"lstm-vs-gru-vs-bidirectional-rnn-for-script","title":"LSTM vs. GRU vs. Bidirectional RNN for script generation","date":"2019-08-12","arxiv_id":"1908.04332","n_code_links":1,"syntology":null},{"paper":"/paper/sentence-specified-dynamic-video-thumbnail","slug":"sentence-specified-dynamic-video-thumbnail","title":"Sentence Specified Dynamic Video Thumbnail Generation","date":"2019-08-12","arxiv_id":"1908.04052","n_code_links":1,"syntology":null},{"paper":null,"slug":"data-driven-predictive-modeling-of-neuronal","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","date":"2019-08-11","arxiv_id":"1908.07428","n_code_links":0,"syntology":null},{"paper":null,"slug":"modeling-engagement-dynamics-of-online","title":"Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory","date":"2019-08-10","arxiv_id":"1908.03770","n_code_links":0,"syntology":null},{"paper":null,"slug":"show-me-your-account-detecting-mmorpg-game","title":"Show Me Your Account: Detecting MMORPG Game Bot Leveraging Financial Analysis with LSTM","date":"2019-08-10","arxiv_id":"1908.03748","n_code_links":0,"syntology":null},{"paper":null,"slug":"lstm-based-flow-prediction","title":"LSTM-based Flow Prediction","date":"2019-08-09","arxiv_id":"1908.03571","n_code_links":0,"syntology":null},{"paper":null,"slug":"scene-based-factored-attention-for-image","title":"Scene-based Factored Attention for Image Captioning","date":"2019-08-07","arxiv_id":"1908.02632","n_code_links":0,"syntology":null},{"paper":null,"slug":"addressing-data-bias-problems-for-chest-x-ray","title":"Addressing Data Bias Problems for Chest X-ray Image Report Generation","date":"2019-08-06","arxiv_id":"1908.02123","n_code_links":0,"syntology":null},{"paper":null,"slug":"classification-of-hand-movements-from-eeg","title":"Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network","date":"2019-08-06","arxiv_id":"1908.02252","n_code_links":0,"syntology":null},{"paper":"/paper/beyond-english-only-reading-comprehension","slug":"beyond-english-only-reading-comprehension","title":"Beyond English-Only Reading Comprehension: Experiments in Zero-Shot Multilingual Transfer for Bulgarian","date":"2019-08-05","arxiv_id":"1908.01519","n_code_links":1,"syntology":null},{"paper":"/paper/squeezenas-fast-neural-architecture-search","slug":"squeezenas-fast-neural-architecture-search","title":"SqueezeNAS: Fast neural architecture search for faster semantic segmentation","date":"2019-08-05","arxiv_id":"1908.01748","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"semi-supervised-thai-sentence-segmentation","title":"Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations","date":"2019-08-04","arxiv_id":"1908.01294","n_code_links":0,"syntology":null},{"paper":"/paper/real-time-deep-learning-at-the-edge-for","slug":"real-time-deep-learning-at-the-edge-for","title":"Real-time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters","date":"2019-08-03","arxiv_id":"1908.01244","n_code_links":1,"syntology":null},{"paper":null,"slug":"risk-management-via-anomaly-circumvent","title":"Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction","date":"2019-08-03","arxiv_id":"1908.01112","n_code_links":0,"syntology":null},{"paper":"/paper/automl-a-survey-of-the-state-of-the-art","slug":"automl-a-survey-of-the-state-of-the-art","title":"AutoML: A Survey of the State-of-the-Art","date":"2019-08-02","arxiv_id":"1908.00709","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["marsggbo/automl_a_survey_of_state_of_the_art"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"dawn-dual-augmented-memory-network-for","title":"DAWN: Dual Augmented Memory Network for Unsupervised Video Object Tracking","date":"2019-08-02","arxiv_id":"1908.00777","n_code_links":0,"syntology":null},{"paper":null,"slug":"dialogue-act-classification-in-group-chats","title":"Dialogue Act Classification in Group Chats with DAG-LSTMs","date":"2019-08-02","arxiv_id":"1908.01821","n_code_links":0,"syntology":null},{"paper":"/paper/greedy-autoaugment","slug":"greedy-autoaugment","title":"Greedy AutoAugment","date":"2019-08-02","arxiv_id":"1908.00704","n_code_links":2,"syntology":null},{"paper":"/paper/lstm-based-music-generation-system","slug":"lstm-based-music-generation-system","title":"LSTM Based Music Generation System","date":"2019-08-02","arxiv_id":"1908.01080","n_code_links":1,"syntology":null},{"paper":null,"slug":"retrosynthesis-with-attention-based-nmt-model","title":"Retrosynthesis with Attention-Based NMT Model and Chemical Analysis of the \"Wrong\" Predictions","date":"2019-08-02","arxiv_id":"1908.00727","n_code_links":0,"syntology":null},{"paper":"/paper/a-hierarchically-labeled-portuguese-hate","slug":"a-hierarchically-labeled-portuguese-hate","title":"A Hierarchically-Labeled Portuguese Hate Speech Dataset","date":"2019-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"a-paraphrase-generation-system-for-ehr","title":"A Paraphrase Generation System for EHR Question Answering","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/an-empirical-study-on-pre-trained-embeddings","slug":"an-empirical-study-on-pre-trained-embeddings","title":"An Empirical Study on Pre-trained Embeddings and Language Models for Bot Detection","date":"2019-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"augmenting-named-entity-recognition-with","title":"Augmenting Named Entity Recognition with Commonsense Knowledge","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cbnu-system-for-sigmorphon-2019-shared-task-2","title":"CBNU System for SIGMORPHON 2019 Shared Task 2: a Pipeline Model","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/convolutional-auto-encoding-of-sentence","slug":"convolutional-auto-encoding-of-sentence","title":"Convolutional Auto-encoding of Sentence Topics for Image Paragraph Generation","date":"2019-08-01","arxiv_id":"1908.00249","n_code_links":0,"syntology":null},{"paper":"/paper/cross-lingual-lemmatization-and-morphology","slug":"cross-lingual-lemmatization-and-morphology","title":"Cross-Lingual Lemmatization and Morphology Tagging with Two-Stage Multilingual BERT Fine-Tuning","date":"2019-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"cuni-malta-system-at-sigmorphon-2019-shared","title":"CUNI--Malta system at SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context: Operation-based word formation","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-of-adverse-drug-reaction-in-tweets","title":"Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-of-adverse-drug-reaction-mentions","title":"Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"identifying-adverse-drug-events-mentions-in","title":"Identifying Adverse Drug Events Mentions in Tweets Using Attentive, Collocated, and Aggregated Medical Representation","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"isolating-the-effects-of-modeling-recursive","title":"Isolating the Effects of Modeling Recursive Structures: A Case Study in Pronunciation Prediction of Chinese Characters","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-the-dyck-language-with-attention","title":"Learning the Dyck Language with Attention-based Seq2Seq Models","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ncuee-at-mediqa-2019-medical-text-inference","title":"NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT-BiLSTM-Attention Model","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-models-for-detecting-binary-semantic","title":"Neural Models for Detecting Binary Semantic Textual Similarity for Algerian and MSA","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"nlpuned-at-smm4h-2019-neural-networks-applied","title":"NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"relating-rnn-layers-with-the-spectral-wfa","title":"Relating RNN Layers with the Spectral WFA Ranks in Sequence Modelling","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"source-source-conditional-elmo-style-model","title":"SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"speech-recognition-for-tigrinya-language","title":"Speech Recognition for Tigrinya language Using Deep Neural Network Approach","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"the-role-of-protected-class-word-lists-in","title":"The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-multi-kernel-convolutional-lstm-networks","title":"Deep Multi-Kernel Convolutional LSTM Networks and an Attention-Based Mechanism for Videos","date":"2019-07-30","arxiv_id":"1908.08990","n_code_links":0,"syntology":null},{"paper":"/paper/genesis-generative-scene-inference-and","slug":"genesis-generative-scene-inference-and","title":"GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations","date":"2019-07-30","arxiv_id":"1907.13052","n_code_links":2,"syntology":null},{"paper":null,"slug":"cloudlstm-a-recurrent-neural-model-for","title":"CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting","date":"2019-07-29","arxiv_id":"1907.12410","n_code_links":0,"syntology":null},{"paper":"/paper/neural-mention-detection","slug":"neural-mention-detection","title":"Neural Mention Detection","date":"2019-07-29","arxiv_id":"1907.12524","n_code_links":1,"syntology":null}],"record_sha256":"492dbd694025abb7eb048b49b46f3a5caa5217517dcfb7d1c70e2ccaae5f8d95","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}