{"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/tanh-activation/papers/38","list_of":"/method/tanh-activation","method":"Tanh Activation","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":64,"rows_per_page":100,"rows":[3701,3800],"of":6333,"counts":{"archive_papers_tagged":6333,"with_a_code_link":2134,"where_syntology_ran_a_sample":386,"not_listed_spam_title":0,"listed":6333,"listed_where_code_ran":386,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":324,"every_run_a_failure_of_syntologys_instrument":62,"listed_with_a_run_with_no_instrument_failure":324,"listed_every_run_a_failure_of_syntologys_instrument":62,"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/tanh-activation","prev":"/method/tanh-activation/papers/37","next":"/method/tanh-activation/papers/39","papers":[{"paper":null,"slug":"stacked-bidirectional-and-unidirectional-lstm","title":"Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values","date":"2020-05-24","arxiv_id":"2005.11627","n_code_links":0,"syntology":null},{"paper":"/paper/stronger-baselines-for-grammatical-error","slug":"stronger-baselines-for-grammatical-error","title":"Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model","date":"2020-05-24","arxiv_id":"2005.11849","n_code_links":2,"syntology":null},{"paper":"/paper/a-cnn-lstm-architecture-for-detection-of","slug":"a-cnn-lstm-architecture-for-detection-of","title":"A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans","date":"2020-05-22","arxiv_id":"2005.10992","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-tree-architecture-of-lstm-networks-for","title":"A Tree Architecture of LSTM Networks for Sequential Regression with Missing Data","date":"2020-05-22","arxiv_id":"2005.11353","n_code_links":0,"syntology":null},{"paper":null,"slug":"bootstrapping-named-entity-recognition-in-e","title":"Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning","date":"2020-05-22","arxiv_id":"2005.11075","n_code_links":0,"syntology":null},{"paper":null,"slug":"investigating-label-bias-in-beam-search-for","title":"Investigating Label Bias in Beam Search for Open-ended Text Generation","date":"2020-05-22","arxiv_id":"2005.11009","n_code_links":0,"syntology":null},{"paper":"/paper/retrieval-augmented-generation-for-knowledge","slug":"retrieval-augmented-generation-for-knowledge","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","date":"2020-05-22","arxiv_id":"2005.11401","n_code_links":18,"syntology":{"ran":6,"of":6,"n_ran_checked":5,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/fluent-response-generation-for-conversational","slug":"fluent-response-generation-for-conversational","title":"Fluent Response Generation for Conversational Question Answering","date":"2020-05-21","arxiv_id":"2005.10464","n_code_links":1,"syntology":null},{"paper":null,"slug":"leveraging-text-data-using-hybrid-transformer","title":"Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learning","date":"2020-05-21","arxiv_id":"2005.10407","n_code_links":0,"syntology":null},{"paper":null,"slug":"powering-one-shot-topological-nas-with","title":"Powering One-shot Topological NAS with Stabilized Share-parameter Proxy","date":"2020-05-21","arxiv_id":"2005.10511","n_code_links":0,"syntology":null},{"paper":"/paper/an-lstm-approach-to-predict-migration-based","slug":"an-lstm-approach-to-predict-migration-based","title":"An LSTM approach to Forecast Migration using Google Trends","date":"2020-05-20","arxiv_id":"2005.09902","n_code_links":1,"syntology":null},{"paper":null,"slug":"investigation-of-learning-abilities-on","title":"Investigation of learning abilities on linguistic features in sequence-to-sequence text-to-speech synthesis","date":"2020-05-20","arxiv_id":"2005.10390","n_code_links":0,"syntology":null},{"paper":null,"slug":"map-generation-from-large-scale-incomplete","title":"Map Generation from Large Scale Incomplete and Inaccurate Data Labels","date":"2020-05-20","arxiv_id":"2005.10053","n_code_links":0,"syntology":null},{"paper":null,"slug":"bridging-the-gap-between-training-and-2","title":"Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting","date":"2020-05-19","arxiv_id":"2005.09343","n_code_links":0,"syntology":null},{"paper":null,"slug":"medical-image-generation-using-generative","title":"Medical Image Generation using Generative Adversarial Networks","date":"2020-05-19","arxiv_id":"2005.10687","n_code_links":0,"syntology":null},{"paper":"/paper/should-we-hard-code-the-recurrence-concept-or","slug":"should-we-hard-code-the-recurrence-concept-or","title":"Should we hard-code the recurrence concept or learn it instead ? Exploring the Transformer architecture for Audio-Visual Speech Recognition","date":"2020-05-19","arxiv_id":"2005.09297","n_code_links":1,"syntology":null},{"paper":null,"slug":"basal-glucose-control-in-type-1-diabetes","title":"Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation","date":"2020-05-18","arxiv_id":"2005.09059","n_code_links":0,"syntology":null},{"paper":"/paper/p-sif-document-embeddings-using-partition","slug":"p-sif-document-embeddings-using-partition","title":"P-SIF: Document Embeddings Using Partition Averaging","date":"2020-05-18","arxiv_id":"2005.09069","n_code_links":1,"syntology":null},{"paper":"/paper/quaternion-neural-networks-for-multi-channel","slug":"quaternion-neural-networks-for-multi-channel","title":"Quaternion Neural Networks for Multi-channel Distant Speech Recognition","date":"2020-05-18","arxiv_id":"2005.08566","n_code_links":1,"syntology":null},{"paper":null,"slug":"separation-of-memory-and-processing-in-dual","title":"Separation of Memory and Processing in Dual Recurrent Neural Networks","date":"2020-05-17","arxiv_id":"2005.13971","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-dialogic-instruction-detection-for","title":"Automatic Dialogic Instruction Detection for K-12 Online One-on-one Classes","date":"2020-05-16","arxiv_id":"2006.01204","n_code_links":0,"syntology":null},{"paper":null,"slug":"contextualizing-asr-lattice-rescoring-with","title":"Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model","date":"2020-05-15","arxiv_id":"2005.07394","n_code_links":0,"syntology":null},{"paper":null,"slug":"hnas-hierarchical-neural-architecture-search","title":"Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search","date":"2020-05-15","arxiv_id":"2005.07564","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-evaluation-of-recent-neural-sequence","title":"An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition","date":"2020-05-14","arxiv_id":"2005.07692","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-networks-versus-conventional-filters","title":"Neural Networks Versus Conventional Filters for Inertial-Sensor-based Attitude Estimation","date":"2020-05-14","arxiv_id":"2005.06897","n_code_links":0,"syntology":null},{"paper":null,"slug":"nit-agartala-nlp-team-at-semeval-2020-task-8","title":"NIT-Agartala-NLP-Team at SemEval-2020 Task 8: Building Multimodal Classifiers to tackle Internet Humor","date":"2020-05-14","arxiv_id":"2005.06943","n_code_links":0,"syntology":null},{"paper":"/paper/neural-architecture-search-for-gliomas","slug":"neural-architecture-search-for-gliomas","title":"Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging","date":"2020-05-13","arxiv_id":"2005.06338","n_code_links":1,"syntology":null},{"paper":null,"slug":"fostering-event-compression-using-gated","title":"Fostering Event Compression using Gated Surprise","date":"2020-05-12","arxiv_id":"2005.05704","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-robustness-of-language-encoders","slug":"on-the-robustness-of-language-encoders","title":"On the Robustness of Language Encoders against Grammatical Errors","date":"2020-05-12","arxiv_id":"2005.05683","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["uclanlp/ProbeGrammarRobustness"],"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":"/paper/end-to-end-speech-synthesis-applied-to","slug":"end-to-end-speech-synthesis-applied-to","title":"TTS-Portuguese Corpus: a corpus for speech synthesis in Brazilian Portuguese","date":"2020-05-11","arxiv_id":"2005.05144","n_code_links":1,"syntology":null},{"paper":"/paper/segmenting-scientific-abstracts-into","slug":"segmenting-scientific-abstracts-into","title":"Segmenting Scientific Abstracts into Discourse Categories: A Deep Learning-Based Approach for Sparse Labeled Data","date":"2020-05-11","arxiv_id":"2005.05414","n_code_links":1,"syntology":null},{"paper":"/paper/covid-19-growth-prediction-using-multivariate","slug":"covid-19-growth-prediction-using-multivariate","title":"COVID-19 growth prediction using multivariate long short term memory","date":"2020-05-10","arxiv_id":"2005.04809","n_code_links":1,"syntology":null},{"paper":null,"slug":"distributed-fine-grained-traffic-speed","title":"Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing","date":"2020-05-10","arxiv_id":"2005.04788","n_code_links":0,"syntology":null},{"paper":"/paper/autoclint-the-winning-method-in-autocv","slug":"autoclint-the-winning-method-in-autocv","title":"AutoCLINT: The Winning Method in AutoCV Challenge 2019","date":"2020-05-09","arxiv_id":"2005.04373","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["kakaobrain/autoclint"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"compressing-recurrent-neural-networks-using","title":"Compressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition","date":"2020-05-09","arxiv_id":"2005.04366","n_code_links":0,"syntology":null},{"paper":"/paper/lince-a-centralized-benchmark-for-linguistic","slug":"lince-a-centralized-benchmark-for-linguistic","title":"LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation","date":"2020-05-09","arxiv_id":"2005.04322","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-effective-dynamic-spatio-temporal","title":"An Effective Dynamic Spatio-temporal Framework with Multi-Source Information for Traffic Prediction","date":"2020-05-08","arxiv_id":"2005.05128","n_code_links":0,"syntology":null},{"paper":"/paper/context-sensitive-generation-network-for","slug":"context-sensitive-generation-network-for","title":"Context-Sensitive Generation Network for Handing Unknown Slot Values in Dialogue State Tracking","date":"2020-05-08","arxiv_id":"2005.03923","n_code_links":1,"syntology":null},{"paper":null,"slug":"sentiment-analysis-using-simplified-long","title":"Sentiment Analysis Using Simplified Long Short-term Memory Recurrent Neural Networks","date":"2020-05-08","arxiv_id":"2005.03993","n_code_links":0,"syntology":null},{"paper":"/paper/ntire-2020-challenge-on-spectral","slug":"ntire-2020-challenge-on-spectral","title":"NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image","date":"2020-05-07","arxiv_id":"2005.03412","n_code_links":1,"syntology":null},{"paper":"/paper/deephist-differentiable-joint-and-color","slug":"deephist-differentiable-joint-and-color","title":"DeepHist: Differentiable Joint and Color Histogram Layers for Image-to-Image Translation","date":"2020-05-06","arxiv_id":"2005.03995","n_code_links":1,"syntology":null},{"paper":null,"slug":"edd-efficient-differentiable-dnn-architecture","title":"EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions","date":"2020-05-06","arxiv_id":"2005.02563","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-augmentation-via-mixed-class","title":"Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery","date":"2020-05-05","arxiv_id":"2005.02436","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-contextual-word-level-style","slug":"exploring-contextual-word-level-style","title":"Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer","date":"2020-05-05","arxiv_id":"2005.02049","n_code_links":1,"syntology":null},{"paper":null,"slug":"long-short-term-memory-networks-and-laglasso","title":"Long short-term memory networks and laglasso for bond yield forecasting: Peeping inside the black box","date":"2020-05-05","arxiv_id":"2005.02217","n_code_links":0,"syntology":null},{"paper":"/paper/neural-crf-model-for-sentence-alignment-in","slug":"neural-crf-model-for-sentence-alignment-in","title":"Neural CRF Model for Sentence Alignment in Text Simplification","date":"2020-05-05","arxiv_id":"2005.02324","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["chaojiang06/wiki-auto"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/stereogan-bridging-synthetic-to-real-domain","slug":"stereogan-bridging-synthetic-to-real-domain","title":"StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching","date":"2020-05-05","arxiv_id":"2005.01927","n_code_links":1,"syntology":null},{"paper":null,"slug":"temporal-event-segmentation-using-attention","title":"Spatio-Temporal Event Segmentation and Localization for Wildlife Extended Videos","date":"2020-05-05","arxiv_id":"2005.02463","n_code_links":0,"syntology":null},{"paper":"/paper/distributional-discrepancy-a-metric-for","slug":"distributional-discrepancy-a-metric-for","title":"Distributional Discrepancy: A Metric for Unconditional Text Generation","date":"2020-05-04","arxiv_id":"2005.01282","n_code_links":1,"syntology":null},{"paper":"/paper/rolling-unrolling-lstms-for-action","slug":"rolling-unrolling-lstms-for-action","title":"Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video","date":"2020-05-04","arxiv_id":"2005.02190","n_code_links":2,"syntology":null},{"paper":"/paper/spying-on-your-neighbors-fine-grained-probing","slug":"spying-on-your-neighbors-fine-grained-probing","title":"Spying on your neighbors: Fine-grained probing of contextual embeddings for information about surrounding words","date":"2020-05-04","arxiv_id":"2005.01810","n_code_links":0,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"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","official":null}},{"paper":null,"slug":"influence-paths-for-characterizing-subject","title":"Influence Paths for Characterizing Subject-Verb Number Agreement in LSTM Language Models","date":"2020-05-03","arxiv_id":"2005.01190","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-based-end-to-end-question","slug":"transformer-based-end-to-end-question","title":"Simplifying Paragraph-level Question Generation via Transformer Language Models","date":"2020-05-03","arxiv_id":"2005.01107","n_code_links":4,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/on-the-generalization-effects-of-linear","slug":"on-the-generalization-effects-of-linear","title":"On the Generalization Effects of Linear Transformations in Data Augmentation","date":"2020-05-02","arxiv_id":"2005.00695","n_code_links":2,"syntology":{"ran":6,"of":9,"n_ran_checked":6,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"6 ran (of which 1 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["SenWu/dauphin"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"a-gradient-boosting-seq2seq-system-for-latin","title":"A Gradient Boosting-Seq2Seq System for Latin POS Tagging and Lemmatization","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-passage-to-india-pre-trained-word","title":"``A Passage to India'': Pre-trained Word Embeddings for Indian Languages","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"affection-driven-neural-networks-for","title":"Affection Driven Neural Networks for Sentiment Analysis","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ai-ml-nit-patna-trac-2-deep-learning-approach","title":"AI\\_ML\\_NIT\\_Patna @ TRAC - 2: Deep Learning Approach for Multi-lingual Aggression Identification","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"aia-bde-a-corpus-of-faqs-in-portuguese-and","title":"AIA-BDE: A Corpus of FAQs in Portuguese and their Variations","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"analyzing-elmo-and-distilbert-on-socio","title":"Analyzing ELMo and DistilBERT on Socio-political News Classification","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"augmenting-small-data-to-classify","title":"Augmenting Small Data to Classify Contextualized Dialogue Acts for Exploratory Visualization","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-transcription-challenges-for","title":"Automatic Transcription Challenges for Inuktitut, a Low-Resource Polysynthetic Language","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/bipartite-flat-graph-network-for-nested-named","slug":"bipartite-flat-graph-network-for-nested-named","title":"Bipartite Flat-Graph Network for Nested Named Entity Recognition","date":"2020-05-01","arxiv_id":"2005.00436","n_code_links":1,"syntology":null},{"paper":null,"slug":"building-a-task-oriented-dialog-system-for","title":"Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"class-based-lstm-russian-language-model-with","title":"Class-based LSTM Russian Language Model with Linguistic Information","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/cross-linguistic-syntactic-evaluation-of-word","slug":"cross-linguistic-syntactic-evaluation-of-word","title":"Cross-Linguistic Syntactic Evaluation of Word Prediction Models","date":"2020-05-01","arxiv_id":"2005.00187","n_code_links":2,"syntology":null},{"paper":null,"slug":"czech-historical-named-entity-corpus-v-1-0","title":"Czech Historical Named Entity Corpus v 1.0","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"dependency-parsing-for-urdu-resources","title":"Dependency Parsing for Urdu: Resources, Conversions and Learning","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-sentence-segmentation-in-different","title":"Evaluating Sentence Segmentation in Different Datasets of Neuropsychological Language Tests in Brazilian Portuguese","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluation-metrics-for-headline-generation","title":"Evaluation Metrics for Headline Generation Using Deep Pre-Trained Embeddings","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"facilitating-corpus-usage-making-icelandic","title":"Facilitating Corpus Usage: Making Icelandic Corpora More Accessible for Researchers and Language Users","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"florunito-trac-2-retrofitting-word-embeddings","title":"FlorUniTo@TRAC-2: Retrofitting Word Embeddings on an Abusive Lexicon for Aggressive Language Detection","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"french-contextualized-word-embeddings-with-a","title":"French Contextualized Word-Embeddings with a sip of CaBeRnet: a New French Balanced Reference Corpus","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/from-arabic-sentiment-analysis-to-sarcasm","slug":"from-arabic-sentiment-analysis-to-sarcasm","title":"From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"hedwig-a-named-entity-linker","title":"Hedwig: A Named Entity Linker","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"high-quality-elmo-embeddings-for-seven-less-1","title":"High Quality ELMo Embeddings for Seven Less-Resourced Languages","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-neural-metaphor-detection-with","title":"Improving Neural Metaphor Detection with Visual Datasets","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"integrating-disfluency-based-and-prosodic","title":"Integrating Disfluency-based and Prosodic Features with Acoustics in Automatic Fluency Evaluation of Spontaneous Speech","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"irony-detection-in-persian-language-a","title":"Irony Detection in Persian Language: A Transfer Learning Approach Using Emoji Prediction","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"is-language-modeling-enough-evaluating","title":"Is Language Modeling Enough? Evaluating Effective Embedding Combinations","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"kernel-of-cyclegan-as-a-principal-homogeneous","title":"Kernel of CycleGAN as a principal homogeneous space","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/klej-comprehensive-benchmark-for-polish","slug":"klej-comprehensive-benchmark-for-polish","title":"KLEJ: Comprehensive Benchmark for Polish Language Understanding","date":"2020-05-01","arxiv_id":"2005.00630","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["allegro/klejbenchmark-baselines"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/mtsi-bert-a-session-aware-knowledge-based","slug":"mtsi-bert-a-session-aware-knowledge-based","title":"MTSI-BERT: A Session-aware Knowledge-based Conversational Agent","date":"2020-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-head-monotonic-chunkwise-attention-for","title":"Multi-head Monotonic Chunkwise Attention For Online Speech Recognition","date":"2020-05-01","arxiv_id":"2005.00205","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-lingual-mathematical-word-problem","title":"Multi-lingual Mathematical Word Problem Generation using Long Short Term Memory Networks with Enhanced Input Features","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"mv-ran-multiview-recurrent-aggregation","title":"MV-RAN: Multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/neural-symbolic-reader-scalable-integration","slug":"neural-symbolic-reader-scalable-integration","title":"Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/offensive-language-detection-in-arabic-using","slug":"offensive-language-detection-in-arabic-using","title":"Offensive language detection in Arabic using ULMFiT","date":"2020-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"one-classifier-for-all-ambiguous-words","title":"One Classifier for All Ambiguous Words: Overcoming Data Sparsity by Utilizing Sense Correlations Across Words","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"parsing-as-tagging","title":"Parsing as Tagging","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"recurrent-neural-networks-and-koopman-based","title":"Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence","date":"2020-05-01","arxiv_id":"2005.02762","n_code_links":0,"syntology":null},{"paper":"/paper/remixmatch-semi-supervised-learning-with","slug":"remixmatch-semi-supervised-learning-with","title":"ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring","date":"2020-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/scirex-a-challenge-dataset-for-document-level","slug":"scirex-a-challenge-dataset-for-document-level","title":"SciREX: A Challenge Dataset for Document-Level Information Extraction","date":"2020-05-01","arxiv_id":"2005.00512","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["allenai/SciREX"],"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"]}}},{"paper":null,"slug":"text-categorization-for-conflict-event","title":"Text Categorization for Conflict Event Annotation","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unior-nlp-at-mwsa-task-globalex-2020-siamese","title":"UNIOR NLP at MWSA Task - GlobaLex 2020: Siamese LSTM with Attention for Word Sense Alignment","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"which-model-should-we-use-for-a-real-world","title":"Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net?","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"attribution-analysis-of-grammatical","title":"Attribution Analysis of Grammatical Dependencies in LSTMs","date":"2020-04-30","arxiv_id":"2005.00062","n_code_links":0,"syntology":null},{"paper":null,"slug":"context-based-text-generation-using-lstm","title":"Context based Text-generation using LSTM networks","date":"2020-04-30","arxiv_id":"2005.00048","n_code_links":0,"syntology":null},{"paper":null,"slug":"enriched-pre-trained-transformers-for-joint","title":"Enriched Pre-trained Transformers for Joint Slot Filling and Intent Detection","date":"2020-04-30","arxiv_id":"2004.14848","n_code_links":0,"syntology":null},{"paper":null,"slug":"feedback-u-net-for-cell-image-segmentation","title":"Feedback U-net for Cell Image Segmentation","date":"2020-04-30","arxiv_id":"2004.14581","n_code_links":0,"syntology":null}],"record_sha256":"7788fec9208ffa082fe669556a170bc56091c61ff365d35f375cb3d1dd245ad0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}