{"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/word-embeddings/papers/19","list_of":"/task/word-embeddings","task":"Word Embeddings","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":19,"pages_in_order":41,"rows_per_page":100,"rows":[1801,1900],"of":4002,"counts":{"archive_papers_tagged":4002,"with_a_code_link":1177,"where_syntology_ran_a_sample":155,"not_listed_spam_title":0,"listed":4002,"listed_where_code_ran":155,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":122,"every_run_a_failure_of_syntologys_instrument":33,"listed_with_a_run_with_no_instrument_failure":122,"listed_every_run_a_failure_of_syntologys_instrument":33,"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/word-embeddings","prev":"/task/word-embeddings/papers/18","next":"/task/word-embeddings/papers/20","papers":[{"url":null,"slug":"wove-incorporating-word-order-in-glove-word","title":"WOVe: Incorporating Word Order in GloVe Word Embeddings","date":"2021-05-18","arxiv_id":"2105.08597","repositories_listed":0,"syntology":null},{"url":null,"slug":"coming-to-its-senses-lessons-learned-from","title":"Coming to its senses: Lessons learned from Approximating Retrofitted BERT representations for Word Sense information","date":"2021-05-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-analysis-of-image-caption","title":"Empirical Analysis of Image Caption Generation using Deep Learning","date":"2021-05-14","arxiv_id":"2105.09906","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-offensive-language-1","title":"Multilingual Offensive Language Identification for Low-resource Languages","date":"2021-05-12","arxiv_id":"2105.05996","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-taxonomy-induction-using-entity","title":"Large-scale Taxonomy Induction Using Entity and Word Embeddings","date":"2021-05-04","arxiv_id":"2105.01305","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-gender-debiased-word-embeddings-in","title":"Impact of Gender Debiased Word Embeddings in Language Modeling","date":"2021-05-03","arxiv_id":"2105.00908","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-lemmatize-in-the-word","title":"Learning to Lemmatize in the Word Representation Space","date":"2021-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-hate-speech-detection-based-on","title":"Cross-lingual hate speech detection based on multilingual domain-specific word embeddings","date":"2021-04-30","arxiv_id":"2104.14728","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-political-bias-in-language-models","title":"Mitigating Political Bias in Language Models Through Reinforced Calibration","date":"2021-04-30","arxiv_id":"2104.14795","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-short-survey-of-pre-trained-language-models","title":"A Short Survey of Pre-trained Language Models for Conversational AI-A NewAge in NLP","date":"2021-04-22","arxiv_id":"2104.10810","repositories_listed":0,"syntology":null},{"url":"/paper/deep-clustering-with-measure-propagation","slug":"deep-clustering-with-measure-propagation","title":"Deep Clustering with Measure Propagation","date":"2021-04-18","arxiv_id":"2104.08967","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-sparse-matrix-factorization-for","title":"Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings","date":"2021-04-18","arxiv_id":"2104.08928","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-neural-machine-translation-with-1","title":"From Fully Trained to Fully Random Embeddings: Improving Neural Machine Translation with Compact Word Embedding Tables","date":"2021-04-18","arxiv_id":"2104.08677","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multilabel-approach-to-morphosyntactic","title":"A multilabel approach to morphosyntactic probing","date":"2021-04-17","arxiv_id":"2104.08464","repositories_listed":0,"syntology":null},{"url":null,"slug":"embodying-pre-trained-word-embeddings-through","title":"Embodying Pre-Trained Word Embeddings Through Robot Actions","date":"2021-04-17","arxiv_id":"2104.08521","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequency-based-distortions-in-contextualized","title":"Frequency-based Distortions in Contextualized Word Embeddings","date":"2021-04-17","arxiv_id":"2104.08465","repositories_listed":0,"syntology":null},{"url":null,"slug":"word2rate-training-and-evaluating-multiple","title":"Word2rate: training and evaluating multiple word embeddings as statistical transitions","date":"2021-04-16","arxiv_id":"2104.08173","repositories_listed":0,"syntology":null},{"url":null,"slug":"upb-at-semeval-2021-task-1-combining-deep","title":"UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity Prediction","date":"2021-04-14","arxiv_id":"2104.06983","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-maps-and-metrics-for-science","title":"Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoders","date":"2021-04-13","arxiv_id":"2104.05928","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-dku-system-description-for-the","title":"The DKU System Description for The Interspeech 2021 Auto-KWS Challenge","date":"2021-04-11","arxiv_id":"2104.04993","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-on-natural-language","title":"Machine Learning Based on Natural Language Processing to Detect Cardiac Failure in Clinical Narratives","date":"2021-04-08","arxiv_id":"2104.03934","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistically-significant-detection-of-1","title":"Statistically significant detection of semantic shifts using contextual word embeddings","date":"2021-04-08","arxiv_id":"2104.03776","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-pre-trained-word-embeddings-and","title":"Combining Pre-trained Word Embeddings and Linguistic Features for Sequential Metaphor Identification","date":"2021-04-07","arxiv_id":"2104.03285","repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-trends-of-covid-19-vaccine-beliefs-on","title":"Mining Trends of COVID-19 Vaccine Beliefs on Twitter with Lexical Embeddings","date":"2021-04-02","arxiv_id":"2104.01131","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-word-embeddings-with-self-1","title":"Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"country-level-arabic-dialect-identification","title":"Country-level Arabic Dialect Identification Using Small Datasets with Integrated Machine Learning Techniques and Deep Learning Models","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-transfer-learning-for-hate","title":"Cross-Lingual Transfer Learning for Hate Speech Detection","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-position-and-contextual-word","title":"Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"handling-out-of-vocabulary-problem-in-hangeul","title":"Handling Out-Of-Vocabulary Problem in Hangeul Word Embeddings","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-learning-using-a-combination-of","title":"Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"relwalk-a-latent-variable-model-approach-to-2","title":"RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-aware-transformation-of-short-texts","title":"Semantic-aware transformation of short texts using word embeddings: An application in the Food Computing domain","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-chinese-remainder-theorem-for-compact","title":"The Chinese Remainder Theorem for Compact, Task-Precise, Efficient and Secure Word Embeddings","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-contextual-and-cross-lingual-word","title":"Using contextual and cross-lingual word embeddings to improve variety in template-based NLG for automated journalism","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-approaches-to-relation-triplets","title":"Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey","date":"2021-03-31","arxiv_id":"2103.16929","repositories_listed":0,"syntology":null},{"url":null,"slug":"locally-contextual-nonlinear-crfs-for","title":"Locally-Contextual Nonlinear CRFs for Sequence Labeling","date":"2021-03-30","arxiv_id":"2103.16210","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-analogical-mapping-with","title":"Probabilistic Analogical Mapping with Semantic Relation Networks","date":"2021-03-30","arxiv_id":"2103.16704","repositories_listed":0,"syntology":null},{"url":null,"slug":"extending-multi-sense-word-embedding-to","title":"Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications","date":"2021-03-29","arxiv_id":"2103.15330","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-introduction-to-robust-graph-convolutional","title":"An Introduction to Robust Graph Convolutional Networks","date":"2021-03-27","arxiv_id":"2103.14807","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-word-embeddings-become-endangered","title":"When Word Embeddings Become Endangered","date":"2021-03-24","arxiv_id":"2103.13275","repositories_listed":0,"syntology":null},{"url":null,"slug":"monolingual-and-parallel-corpora-for-kangri","title":"Monolingual and Parallel Corpora for Kangri Low Resource Language","date":"2021-03-22","arxiv_id":"2103.11596","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsegan-sparse-generative-adversarial","title":"SparseGAN: Sparse Generative Adversarial Network for Text Generation","date":"2021-03-22","arxiv_id":"2103.11578","repositories_listed":0,"syntology":null},{"url":null,"slug":"semie-semantically-infused-embeddings-with","title":"SEMIE: SEMantically Infused Embeddings with Enhanced Interpretability for Domain-specific Small Corpus","date":"2021-03-21","arxiv_id":"2103.11431","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-model-for-predicting-question","title":"Attention-based model for predicting question relatedness on Stack Overflow","date":"2021-03-19","arxiv_id":"2103.10763","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-word-embeddings-really-understand-loughran","title":"Do Word Embeddings Really Understand Loughran-McDonald's Polarities?","date":"2021-03-17","arxiv_id":"2103.09813","repositories_listed":0,"syntology":null},{"url":null,"slug":"deephate-hate-speech-detection-via-multi","title":"DeepHate: Hate Speech Detection via Multi-Faceted Text Representations","date":"2021-03-14","arxiv_id":"2103.11799","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-morphological-embeddings-for-1","title":"Evaluation of Morphological Embeddings for the Russian Language","date":"2021-03-11","arxiv_id":"2103.06628","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-transfer-learning-in","title":"Unsupervised Transfer Learning in Multilingual Neural Machine Translation with Cross-Lingual Word Embeddings","date":"2021-03-11","arxiv_id":"2103.06689","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-word2vec-hmm2vec-and-pca2vec","title":"A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification","date":"2021-03-07","arxiv_id":"2103.05763","repositories_listed":0,"syntology":null},{"url":null,"slug":"cnn-based-spoken-term-detection-and","title":"CNN-based Spoken Term Detection and Localization without Dynamic Programming","date":"2021-03-07","arxiv_id":"2103.05468","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-aware-knowledge-distillation-for-few","title":"Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning","date":"2021-03-06","arxiv_id":"2103.04059","repositories_listed":0,"syntology":null},{"url":null,"slug":"overcoming-poor-word-embeddings-with-word","title":"Overcoming Poor Word Embeddings with Word Definitions","date":"2021-03-05","arxiv_id":"2103.03842","repositories_listed":0,"syntology":null},{"url":null,"slug":"lex2vec-making-explainable-word-embedding-via","title":"Lex2vec: making Explainable Word Embeddings via Lexical Resources","date":"2021-03-03","arxiv_id":"2103.02269","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextually-guided-convolutional-neural","title":"CG-CNN: Self-Supervised Feature Extraction Through Contextual Guidance and Transfer Learning","date":"2021-03-02","arxiv_id":"2103.01566","repositories_listed":0,"syntology":null},{"url":null,"slug":"spanish-biomedical-and-clinical-language","title":"Spanish Biomedical and Clinical Language Embeddings","date":"2021-02-25","arxiv_id":"2102.12843","repositories_listed":0,"syntology":null},{"url":null,"slug":"abelian-neural-networks","title":"Abelian Neural Networks","date":"2021-02-24","arxiv_id":"2102.12232","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-sensitivity-of-word-embeddings-based","title":"The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations","date":"2021-02-23","arxiv_id":"2102.11917","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-captioning-using-deep-stacked-lstms","title":"Image Captioning using Deep Stacked LSTMs, Contextual Word Embeddings and Data Augmentation","date":"2021-02-22","arxiv_id":"2102.11237","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-base-enriched-word-embeddings-for","title":"Knowledge-Base Enriched Word Embeddings for Biomedical Domain","date":"2021-02-20","arxiv_id":"2103.00479","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-covid-19-is-changing-our-language","title":"How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddings","date":"2021-02-15","arxiv_id":"2102.07836","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-aware-speaker-embeddings-for-speaker","title":"Content-Aware Speaker Embeddings for Speaker Diarisation","date":"2021-02-12","arxiv_id":"2102.06467","repositories_listed":0,"syntology":null},{"url":null,"slug":"points2vec-unsupervised-object-level-feature","title":"Points2Vec: Unsupervised Object-level Feature Learning from Point Clouds","date":"2021-02-08","arxiv_id":"2102.04136","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-note-on-argumentative-topology-circularity","title":"A Note on Argumentative Topology: Circularity and Syllogisms as Unsolved Problems","date":"2021-02-07","arxiv_id":"2102.03874","repositories_listed":0,"syntology":null},{"url":null,"slug":"bootstrapping-multilingual-amr-with","title":"Bootstrapping Multilingual AMR with Contextual Word Alignments","date":"2021-02-03","arxiv_id":"2102.02189","repositories_listed":0,"syntology":null},{"url":null,"slug":"focusing-knowledge-based-graph-argument","title":"Focusing Knowledge-based Graph Argument Mining via Topic Modeling","date":"2021-02-03","arxiv_id":"2102.02086","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-word-embeddings-to-uncover-discourses","title":"Using Word Embeddings to Uncover Discourses","date":"2021-02-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/short-text-clustering-with-transformers","slug":"short-text-clustering-with-transformers","title":"Short Text Clustering with Transformers","date":"2021-01-31","arxiv_id":"2102.00541","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-disaster-related-knowledge-base-for","title":"A Simple Disaster-Related Knowledge Base for Intelligent Agents","date":"2021-01-25","arxiv_id":"2101.10014","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-intelligence-prediction-of-stock","title":"Artificial intelligence prediction of stock prices using social media","date":"2021-01-22","arxiv_id":"2101.08986","repositories_listed":0,"syntology":null},{"url":null,"slug":"bert-transformer-model-for-detecting-arabic","title":"BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets","date":"2021-01-22","arxiv_id":"2101.09345","repositories_listed":0,"syntology":null},{"url":null,"slug":"censorship-of-online-encyclopedias","title":"Censorship of Online Encyclopedias: Implications for NLP Models","date":"2021-01-22","arxiv_id":"2101.09294","repositories_listed":0,"syntology":null},{"url":null,"slug":"hostility-detection-and-covid-19-fake-news","title":"Hostility Detection and Covid-19 Fake News Detection in Social Media","date":"2021-01-15","arxiv_id":"2101.05953","repositories_listed":0,"syntology":null},{"url":null,"slug":"experimental-evaluation-of-deep-learning","title":"Experimental Evaluation of Deep Learning models for Marathi Text Classification","date":"2021-01-13","arxiv_id":"2101.04899","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-deep-learning-models-for","title":"Evaluation of Deep Learning Models for Hostility Detection in Hindi Text","date":"2021-01-11","arxiv_id":"2101.04144","repositories_listed":0,"syntology":null},{"url":null,"slug":"misspelling-correction-with-pre-trained","title":"Misspelling Correction with Pre-trained Contextual Language Model","date":"2021-01-08","arxiv_id":"2101.03204","repositories_listed":0,"syntology":null},{"url":null,"slug":"integration-of-domain-knowledge-using-medical","title":"Integration of Domain Knowledge using Medical Knowledge Graph Deep Learning for Cancer Phenotyping","date":"2021-01-05","arxiv_id":"2101.01337","repositories_listed":0,"syntology":null},{"url":null,"slug":"political-depolarization-of-news-articles","title":"Political Depolarization of News Articles Using Attribute-aware Word Embeddings","date":"2021-01-05","arxiv_id":"2101.01391","repositories_listed":0,"syntology":null},{"url":null,"slug":"lex-bert-enhancing-bert-based-ner-with","title":"Lex-BERT: Enhancing BERT based NER with lexicons","date":"2021-01-02","arxiv_id":"2101.00396","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-taxonomy-enrichment-on","title":"Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"faster-training-of-word-embeddings","title":"Faster Training of Word Embeddings","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-methods-in-hyperbolic-spaces","title":"Kernel Methods in Hyperbolic Spaces","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"key-phrase-extraction-applause-prediction","title":"Key Phrase Extraction & Applause Prediction","date":"2021-01-01","arxiv_id":"2101.03235","repositories_listed":0,"syntology":null},{"url":null,"slug":"ruminating-word-representations-with-random-1","title":"Ruminating Word Representations with Random Noise Masking","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"text-document-clustering-wordnet-vs-tf-idf-vs","title":"Text Document Clustering: Wordnet vs. TF-IDF vs. Word Embeddings","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"topic-aware-contextualized-transformers","title":"Topic-aware Contextualized Transformers","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tracking-the-progress-of-language-models-by","title":"Tracking the progress of Language Models by extracting their underlying Knowledge Graphs","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-textual-attentive-semantic-consistency","title":"Visual-Textual Attentive Semantic Consistency for Medical Report Generation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-offline-mapping-learning-cross-lingual","title":"Beyond Offline Mapping: Learning Cross Lingual Word Embeddings through Context Anchoring","date":"2020-12-31","arxiv_id":"2012.15715","repositories_listed":0,"syntology":null},{"url":null,"slug":"intrinsic-bias-metrics-do-not-correlate-with","title":"Intrinsic Bias Metrics Do Not Correlate with Application Bias","date":"2020-12-31","arxiv_id":"2012.15859","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeing-is-knowing-fact-based-visual-question","title":"Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings","date":"2020-12-31","arxiv_id":"2012.15484","repositories_listed":0,"syntology":null},{"url":null,"slug":"deriving-contextualised-semantic-features","title":"Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings","date":"2020-12-30","arxiv_id":"2012.15353","repositories_listed":0,"syntology":null},{"url":null,"slug":"wembsim-a-simple-yet-effective-metric-for","title":"WEmbSim: A Simple yet Effective Metric for Image Captioning","date":"2020-12-24","arxiv_id":"2012.13137","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-self-supervised-speech","title":"A comparison of self-supervised speech representations as input features for unsupervised acoustic word embeddings","date":"2020-12-14","arxiv_id":"2012.07387","repositories_listed":0,"syntology":null},{"url":null,"slug":"intrinsic-image-captioning-evaluation","title":"Intrinsic Image Captioning Evaluation","date":"2020-12-14","arxiv_id":"2012.07333","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-choices-influence-attributive-word","title":"Model Choices Influence Attributive Word Associations: A Semi-supervised Analysis of Static Word Embeddings","date":"2020-12-14","arxiv_id":"2012.07978","repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-pre-training-for-low-resource","title":"Discriminative Pre-training for Low Resource Title Compression in Conversational Grocery","date":"2020-12-13","arxiv_id":"2012.06943","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-zero-shot-learning-baselines-with","title":"Improving Zero Shot Learning Baselines with Commonsense Knowledge","date":"2020-12-11","arxiv_id":"2012.06236","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-word-sense-disambiguation-using","title":"Cross-lingual Word Sense Disambiguation using mBERT Embeddings with Syntactic Dependencies","date":"2020-12-09","arxiv_id":"2012.05300","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-correspondence-variational-autoencoder-for","title":"A Correspondence Variational Autoencoder for Unsupervised Acoustic Word Embeddings","date":"2020-12-03","arxiv_id":"2012.02221","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-computational-approach-to-measuring-the","title":"A Computational Approach to Measuring the Semantic Divergence of Cognates","date":"2020-12-02","arxiv_id":"2012.01288","repositories_listed":0,"syntology":null}],"record_sha256":"d440eb941f8d19ca8c77d5ab912490b4f94f0a91534e3380e39c2de875520336","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}