{"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/part-of-speech-tagging/papers/4","list_of":"/task/part-of-speech-tagging","task":"Part-Of-Speech Tagging","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":4,"pages_in_order":10,"rows_per_page":100,"rows":[301,400],"of":990,"counts":{"archive_papers_tagged":990,"with_a_code_link":228,"where_syntology_ran_a_sample":28,"not_listed_spam_title":0,"listed":990,"listed_where_code_ran":28,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":24,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":24,"listed_every_run_a_failure_of_syntologys_instrument":4,"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/part-of-speech-tagging","prev":"/task/part-of-speech-tagging/papers/3","next":"/task/part-of-speech-tagging/papers/5","papers":[{"url":null,"slug":"using-cross-lingual-part-of-speech-tagging","title":"Using Cross-Lingual Part of Speech Tagging for Partially Reconstructing the Classic Language Family Tree Model","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"handling-and-presenting-harmful-text","title":"Handling and Presenting Harmful Text in NLP Research","date":"2022-04-29","arxiv_id":"2204.14256","repositories_listed":0,"syntology":null},{"url":null,"slug":"yunshan-cup-2020-overview-of-the-part-of","title":"Yunshan Cup 2020: Overview of the Part-of-Speech Tagging Task for Low-resourced Languages","date":"2022-04-06","arxiv_id":"2204.02658","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-token-segmentation-for-high-token","title":"Neural Token Segmentation for High Token-Internal Complexity","date":"2022-03-21","arxiv_id":"2203.10845","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-freem-to-d-alembert-a-large-corpus-and-a","title":"From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French","date":"2022-02-18","arxiv_id":"2202.09452","repositories_listed":0,"syntology":null},{"url":null,"slug":"part-of-speech-tagging-post-of-a-low-resource","title":"Part of Speech Tagging (POST) of a Low-resource Language using another Language (Developing a POS-Tagged Lexicon for Kurdish (Sorani) using a Tagged Persian (Farsi) Corpus)","date":"2022-01-30","arxiv_id":"2201.12793","repositories_listed":0,"syntology":null},{"url":null,"slug":"surprisingly-simple-adapter-ensembling-for","title":"Surprisingly Simple Adapter Ensembling for Zero-Shot Cross-Lingual Sequence Tagging","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-warm-start-and-a-clean-crawled-corpus-a","title":"A Warm Start and a Clean Crawled Corpus -- A Recipe for Good Language Models","date":"2022-01-14","arxiv_id":"2201.05601","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-dataset-and-dictionary-sizes-matter","title":"Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages","date":"2021-12-20","arxiv_id":"2112.10553","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-chinese-word-segmentation-and-part-of-2","title":"Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-stage Span Labeling","date":"2021-12-17","arxiv_id":"2112.09488","repositories_listed":0,"syntology":null},{"url":null,"slug":"part-of-speech-tagging-for-a-resource-poor","title":"Part of Speech Tagging for a Resource Poor Language : Sindhi in Devanagari Script using HMM and CRF","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"to-augment-or-not-to-augment-a-comparative","title":"To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP","date":"2021-11-18","arxiv_id":"2111.09618","repositories_listed":0,"syntology":null},{"url":null,"slug":"make-the-best-of-cross-lingual-transfer","title":"Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-prosody-for-unseen-texts-in-speech","title":"Improving Prosody for Unseen Texts in Speech Synthesis by Utilizing Linguistic Information and Noisy Data","date":"2021-11-15","arxiv_id":"2111.07549","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-text-editing-approach-to-joint-japanese","title":"A Text Editing Approach to Joint Japanese Word Segmentation, POS Tagging, and Lexical Normalization","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"can-character-based-language-models-improve-1","title":"Can Character-based Language Models Improve Downstream Task Performances In Low-Resource And Noisy Language Scenarios?","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-transformer-architecture-for","title":"Deep Learning Transformer Architecture for Named Entity Recognition on Low Resourced Languages: State of the art results","date":"2021-11-01","arxiv_id":"2111.00830","repositories_listed":0,"syntology":null},{"url":null,"slug":"mad-g-multilingual-adapter-generation-for","title":"MAD-G: Multilingual Adapter Generation for Efficient Cross-Lingual Transfer","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sequence-mixup-for-zero-shot-cross-lingual","title":"Sequence Mixup for Zero-Shot Cross-Lingual Part-Of-Speech Tagging","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"can-character-based-language-models-improve","title":"Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?","date":"2021-10-26","arxiv_id":"2110.13658","repositories_listed":0,"syntology":null},{"url":null,"slug":"laoplm-pre-trained-language-models-for-lao","title":"LaoPLM: Pre-trained Language Models for Lao","date":"2021-10-12","arxiv_id":"2110.05896","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-on-part-of-speech-technologies","title":"A Review on Part-of-Speech Technologies","date":"2021-10-11","arxiv_id":"2110.04977","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-sequence-labeling-models-using-prior","title":"Training sequence labeling models using prior knowledge","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-register-projection-for-headline-part","title":"Cross-Register Projection for Headline Part of Speech Tagging","date":"2021-09-15","arxiv_id":"2109.07483","repositories_listed":0,"syntology":null},{"url":null,"slug":"combo-state-of-the-art-morphosyntactic","title":"COMBO: State-of-the-Art Morphosyntactic Analysis","date":"2021-09-11","arxiv_id":"2109.05361","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-much-pretraining-data-do-language-models","title":"How much pretraining data do language models need to learn syntax?","date":"2021-09-07","arxiv_id":"2109.03160","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-pre-trained-transformer-and-cnn-model-with","title":"A Pre-trained Transformer and CNN Model with Joint Language ID and Part-of-Speech Tagging for Code-Mixed Social-Media Text","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"comboner-a-lightweight-all-in-one-pos-tagger","title":"ComboNER: A Lightweight All-In-One POS Tagger, Dependency Parser and NER","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"corpus-creation-and-language-identification","title":"Corpus Creation and Language Identification in Low-Resource Code-Mixed Telugu-English Text","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-neural-language-processing-with","title":"Improving Neural Language Processing with Named Entities","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sipos-a-benchmark-dataset-for-sindhi-part-of","title":"SiPOS: A Benchmark Dataset for Sindhi Part-of-Speech Tagging","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"text-preprocessing-and-its-implications-in-a","title":"Text Preprocessing and its Implications in a Digital Humanities Project","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uzbert-pretraining-a-bert-model-for-uzbek","title":"UzBERT: pretraining a BERT model for Uzbek","date":"2021-08-22","arxiv_id":"2108.09814","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-morphological-typology-in-zero","title":"Evaluating morphological typology in zero-shot cross-lingual transfer","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"preserving-cross-linguality-of-pre-trained","title":"Preserving Cross-Linguality of Pre-trained Models via Continual Learning","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-automated-domain-independent-text-reading","title":"An automated domain-independent text reading, interpreting and extracting approach for reviewing the scientific literature","date":"2021-07-30","arxiv_id":"2107.14638","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-multilingual-models-the-best-choice-for","title":"Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan","date":"2021-07-16","arxiv_id":"2107.07903","repositories_listed":0,"syntology":null},{"url":"/paper/dacy-a-unified-framework-for-danish-nlp","slug":"dacy-a-unified-framework-for-danish-nlp","title":"DaCy: A Unified Framework for Danish NLP","date":"2021-07-12","arxiv_id":"2107.05295","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-supervised-domain-adaptation-by","title":"Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units","date":"2021-06-09","arxiv_id":"2106.04935","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-adapt-your-pretrained-multilingual","title":"How to Adapt Your Pretrained Multilingual Model to 1600 Languages","date":"2021-06-03","arxiv_id":"2106.02124","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-part-of-speech-tagging-approaches","title":"A survey of part-of-speech tagging approaches applied to K’iche’","date":"2021-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"normalization-and-back-transliteration-for","title":"Normalization and Back-Transliteration for Code-Switched Data","date":"2021-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pos-tagging-based-neural-machine-translation","title":"POS-Tagging based Neural Machine Translation System for European Languages using Transformers","date":"2021-05-31","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-planetary-systems-as-ordered-sequences","title":"On planetary systems as ordered sequences","date":"2021-05-20","arxiv_id":"2105.09966","repositories_listed":0,"syntology":null},{"url":"/paper/bertic-the-transformer-language-model-for","slug":"bertic-the-transformer-language-model-for","title":"BERTić -- The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian","date":"2021-04-19","arxiv_id":"2104.09243","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-size-performance-tradeoffs-weighing","title":"Optimal Size-Performance Tradeoffs: Weighing PoS Tagger Models","date":"2021-04-16","arxiv_id":"2104.07951","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-resource-multi-dialectal-arabic-natural","title":"Zero-Resource Multi-Dialectal Arabic Natural Language Understanding","date":"2021-04-14","arxiv_id":"2104.06591","repositories_listed":0,"syntology":null},{"url":null,"slug":"bertic-the-transformer-language-model-for-1","title":"BERTić - The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-tasks-integration-tagging-syntactic","title":"Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-hidden-negative-transfer-in-sequential","title":"On the Hidden Negative Transfer in Sequential Transfer Learning for Domain Adaptation from News to Tweets","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-khmer-word-segmentation-and-part-of","title":"Joint Khmer Word Segmentation and Part-of-Speech Tagging Using Deep Learning","date":"2021-03-31","arxiv_id":"2103.16801","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-text-embeddings-for-twi","title":"Contextual Text Embeddings for Twi","date":"2021-03-29","arxiv_id":"2103.15963","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-multilingual-models-effective-in-code","title":"Are Multilingual Models Effective in Code-Switching?","date":"2021-03-24","arxiv_id":"2103.13309","repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-the-hidden-neural-markov-chain","title":"Introducing the Hidden Neural Markov Chain framework","date":"2021-02-17","arxiv_id":"2102.11038","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-more-efficient-chinese-named-entity","title":"A More Efficient Chinese Named Entity Recognition base on BERT and Syntactic Analysis","date":"2021-01-11","arxiv_id":"2101.11423","repositories_listed":0,"syntology":null},{"url":null,"slug":"substructure-substitution-structured-data","title":"Substructure Substitution: Structured Data Augmentation for NLP","date":"2021-01-02","arxiv_id":"2101.00411","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmenting-natural-language-sentences-via-1","title":"Segmenting Natural Language Sentences via Lexical Unit Analysis","date":"2020-12-10","arxiv_id":"2012.05418","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-chinese-parsing-exploiting","title":"End-to-End Chinese Parsing Exploiting Lexicons","date":"2020-12-08","arxiv_id":"2012.04395","repositories_listed":0,"syntology":null},{"url":null,"slug":"developing-a-faroese-pos-tagging-solution","title":"Developing a Faroese PoS-tagging solution using Icelandic methods","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"embed-more-ignore-less-emil-exploiting","title":"Embed More Ignore Less (EMIL): Exploiting Enriched Representations for Arabic NLP","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-language-of-data","title":"Exploring the Language of Data","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"named-entity-based-sentiment-analysis-of","title":"Named-Entity Based Sentiment Analysis of Nepali News Media Texts","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"persuasive-dialogue-understanding-the","title":"Persuasive Dialogue Understanding: the Baselines and Negative Results","date":"2020-11-19","arxiv_id":"2011.09954","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-recent-advances-in-sequence","title":"A Survey on Recent Advances in Sequence Labeling from Deep Learning Models","date":"2020-11-13","arxiv_id":"2011.06727","repositories_listed":0,"syntology":null},{"url":null,"slug":"daga-data-augmentation-with-a-generation","title":"DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks","date":"2020-11-03","arxiv_id":"2011.01549","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-confusion-in-active-learning-for","title":"Reducing Confusion in Active Learning for Part-Of-Speech Tagging","date":"2020-11-02","arxiv_id":"2011.00767","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-analysis-of-capsule-networks-for-part-of","title":"An Analysis of Capsule Networks for Part of Speech Tagging in High- and Low-resource Scenarios","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/sparsity-makes-sense-word-sense","slug":"sparsity-makes-sense-word-sense","title":"Sparsity Makes Sense: Word Sense Disambiguation Using Sparse Contextualized Word Representations","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-cross-lingual-part-of-speech","title":"Unsupervised Cross-Lingual Part-of-Speech Tagging for Truly Low-Resource Scenarios","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-adequate-distractors-for-multiple","title":"Generating Adequate Distractors for Multiple-Choice Questions","date":"2020-10-23","arxiv_id":"2010.12658","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-sequence-learning-and-its-applications","title":"Meta Sequence Learning for Generating Adequate Question-Answer Pairs","date":"2020-10-04","arxiv_id":"2010.01620","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-domain-adaption-using","title":"Attention-based Domain Adaption Using Transfer Learning for Part-of-Speech Tagging: An Experiment on the Hindi language","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-models-for-representing-out-of","title":"Deep learning models for representing out-of-vocabulary words","date":"2020-07-14","arxiv_id":"2007.07318","repositories_listed":0,"syntology":null},{"url":"/paper/a-monolingual-approach-to-contextualized-word","slug":"a-monolingual-approach-to-contextualized-word","title":"A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages","date":"2020-06-11","arxiv_id":"2006.06202","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multitask-learning-approach-for-diacritic","title":"A Multitask Learning Approach for Diacritic Restoration","date":"2020-06-07","arxiv_id":"2006.04016","repositories_listed":0,"syntology":null},{"url":null,"slug":"hidden-markov-chains-entropic-forward","title":"Hidden Markov Chains, Entropic Forward-Backward, and Part-Of-Speech Tagging","date":"2020-05-21","arxiv_id":"2005.10629","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-choice-of-auxiliary-languages-for","title":"On the Choice of Auxiliary Languages for Improved Sequence Tagging","date":"2020-05-19","arxiv_id":"2005.09389","repositories_listed":0,"syntology":null},{"url":"/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","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptation-of-deep-bidirectional-transformers","title":"Adaptation of Deep Bidirectional Transformers for Afrikaans Language","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"can-multilingual-language-models-transfer-to","title":"Can Multilingual Language Models Transfer to an Unseen Dialect? A Case Study on North African Arabizi","date":"2020-05-01","arxiv_id":"2005.00318","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-choices-in-neural-part-of-speech","title":"Data-driven Choices in Neural Part-of-Speech Tagging for Latin","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"integration-of-automatic-sentence","title":"Integration of Automatic Sentence Segmentation and Lexical Analysis of Ancient Chinese based on BiLSTM-CRF Model","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"jhubc-s-submission-to-lt4hala-evalatin-2020","title":"JHUBC's Submission to LT4HALA EvaLatin 2020","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ofrlex-a-computational-morphological-and","title":"OFrLex: A Computational Morphological and Syntactic Lexicon for Old French","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"overview-of-the-evalatin-2020-evaluation","title":"Overview of the EvaLatin 2020 Evaluation Campaign","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reproducing-a-morphosyntactic-tagger-with-a","title":"Reproducing a Morphosyntactic Tagger with a Meta-BiLSTM Model over Context Sensitive Token Encodings","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unfinished-business-construction-and","title":"Unfinished Business: Construction and Maintenance of a Semantically Tagged Historical Parliamentary Corpus, UK Hansard from 1803 to the present day","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"voting-for-pos-tagging-of-latin-texts-using","title":"Voting for POS tagging of Latin texts: Using the flair of FLAIR to better Ensemble Classifiers by Example of Latin","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"word-embedding-evaluation-for-sinhala","title":"Word Embedding Evaluation for Sinhala","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-fine-tuning-techniques-for-pre","title":"Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning","date":"2020-04-29","arxiv_id":"2004.14218","repositories_listed":0,"syntology":null},{"url":"/paper/igbo-english-machine-translation-an","slug":"igbo-english-machine-translation-an","title":"Igbo-English Machine Translation: An Evaluation Benchmark","date":"2020-04-01","arxiv_id":"2004.00648","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-to-image-translation-with-text-guidance","title":"Image-to-Image Translation with Text Guidance","date":"2020-02-12","arxiv_id":"2002.05235","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-we-need-word-order-information-for-cross","title":"On the Importance of Word Order Information in Cross-lingual Sequence Labeling","date":"2020-01-30","arxiv_id":"2001.11164","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-approaches-for-amharic-parts","title":"Machine Learning Approaches for Amharic Parts-of-speech Tagging","date":"2020-01-10","arxiv_id":"2001.03324","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-architectures-and-pretraining","title":"A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings","date":"2019-12-15","arxiv_id":"1912.10169","repositories_listed":0,"syntology":null},{"url":null,"slug":"homograph-disambiguation-through-selective-1","title":"Homograph Disambiguation Through Selective Diacritic Restoration","date":"2019-12-10","arxiv_id":"1912.04479","repositories_listed":0,"syntology":null},{"url":null,"slug":"design-and-implementation-of-an-open-source","title":"Design and implementation of an open source Greek POS Tagger and Entity Recognizer using spaCy","date":"2019-12-05","arxiv_id":"1912.10162","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-rich-part-of-speech-tagging-for-1","title":"Feature-Rich Part-of-speech Tagging for Morphologically Complex Languages: Application to Bulgarian","date":"2019-11-26","arxiv_id":"1911.11503","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-vietnamese-name-entity-recognition-a","title":"On the Vietnamese Name Entity Recognition: A Deep Learning Method Approach","date":"2019-11-18","arxiv_id":"1912.01109","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-dictionary-feature-into-a-deep","title":"Integrating Dictionary Feature into A Deep Learning Model for Disease Named Entity Recognition","date":"2019-11-05","arxiv_id":"1911.01600","repositories_listed":0,"syntology":null}],"record_sha256":"1b4f808fcce84d8bec72f89fd6b4378468493e46a35e6895fbc5f3d078d3e894","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}