{"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/task-2/papers/4","list_of":"/task/task-2","task":"Task 2","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":6,"rows_per_page":100,"rows":[301,400],"of":572,"counts":{"archive_papers_tagged":572,"with_a_code_link":154,"where_syntology_ran_a_sample":14,"not_listed_spam_title":0,"listed":572,"listed_where_code_ran":14,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":11,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":11,"listed_every_run_a_failure_of_syntologys_instrument":3,"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/task-2","prev":"/task/task-2/papers/3","next":"/task/task-2/papers/5","papers":[{"url":null,"slug":"overview-of-the-clef-2019-checkthat-automatic","title":"Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims","date":"2021-09-25","arxiv_id":"2109.15118","repositories_listed":0,"syntology":null},{"url":null,"slug":"findings-of-the-nlp4if-2021-shared-tasks-on","title":"Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection","date":"2021-09-23","arxiv_id":"2109.12986","repositories_listed":0,"syntology":null},{"url":null,"slug":"overview-of-the-clef-2021-checkthat-lab-on","title":"Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News","date":"2021-09-23","arxiv_id":"2109.12987","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-jhu-microsoft-submission-for-wmt21","title":"The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task","date":"2021-09-17","arxiv_id":"2109.08724","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-biomedical-bert-models-for","title":"Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus","date":"2021-09-14","arxiv_id":"2109.13348","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulating-the-effects-of-eco-friendly","title":"Simulating the Effects of Eco-Friendly Transportation Selections for Air Pollution Reduction","date":"2021-09-10","arxiv_id":"2109.04831","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-fine-tuned-mbert-for-translation","title":"Ensemble Fine-tuned mBERT for Translation Quality Estimation","date":"2021-09-08","arxiv_id":"2109.03914","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-pre-training-strategy-for-zero-resource","title":"A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptor-grammars-for-unsupervised-paradigm","title":"Adaptor Grammars for Unsupervised Paradigm Clustering","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cambridge-at-semeval-2021-task-2-neural-wic","title":"Cambridge at SemEval-2021 Task 2: Neural WiC-Model with Data Augmentation and Exploration of Representation","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"case-2021-task-2-socio-political-fine-grained","title":"CASE 2021 Task 2 Socio-political Fine-grained Event Classification using Fine-tuned RoBERTa Document Embeddings","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"case-2021-task-2-zero-shot-classification-of","title":"CASE 2021 Task 2: Zero-Shot Classification of Fine-Grained Sociopolitical Events with Transformer Models","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fii-cross-at-semeval-2021-task-2-multilingual","title":"FII\\_CROSS at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-event-classification-in-news","title":"Fine-grained Event Classification in News-like Text Snippets - Shared Task 2, CASE 2021","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"glossreader-at-semeval-2021-task-2-reading","title":"GlossReader at SemEval-2021 Task 2: Reading Definitions Improves Contextualized Word Embeddings","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gulu-at-semeval-2021-task-7-detecting-and","title":"Gulu at SemEval-2021 Task 7: Detecting and Rating Humor and Offense","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hub-at-semeval-2021-task-2-word-meaning","title":"hub at SemEval-2021 Task 2: Word Meaning Similarity Prediction Model Based on RoBERTa and Word Frequency","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ibm-mnlp-ie-at-case-2021-task-2-nli-reranking","title":"IBM MNLP IE at CASE 2021 Task 2: NLI Reranking for Zero-Shot Text Classification","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iitk-lcp-at-semeval-2021-task-1-1","title":"IITK@LCP at SemEval-2021 Task 1: Classification for Lexical Complexity Regression Task","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"liori-at-semeval-2021-task-2-span-prediction","title":"LIORI at SemEval-2021 Task 2: Span Prediction and Binary Classification approaches to Word-in-Context Disambiguation","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lotus-at-semeval-2021-task-2-combination-of","title":"Lotus at SemEval-2021 Task 2: Combination of BERT and Paraphrasing for English Word Sense Disambiguation","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-protest-news-detection-shared","title":"Multilingual Protest News Detection - Shared Task 1, CASE 2021","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"orthographic-vs-semantic-representations-for","title":"Orthographic vs. Semantic Representations for Unsupervised Morphological Paradigm Clustering","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"paw-at-semeval-2021-task-2-multilingual-and","title":"PAW at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation : Exploring Cross Lingual Transfer, Augmentations and Adversarial Training","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rg-pa-at-semeval-2021-task-1-a-contextual","title":"RG PA at SemEval-2021 Task 1: A Contextual Attention-based Model with RoBERTa for Lexical Complexity Prediction","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sarcasmdet-at-semeval-2021-task-7-detect","title":"SarcasmDet at SemEval-2021 Task 7: Detect Humor and Offensive based on Demographic Factors using RoBERTa Pre-trained Model","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"skoltechnlp-at-semeval-2021-task-2-generating","title":"SkoltechNLP at SemEval-2021 Task 2: Generating Cross-Lingual Training Data for the Word-in-Context Task","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"szegedai-at-semeval-2021-task-2-zero-shot","title":"SzegedAI at SemEval-2021 Task 2: Zero-shot Approach for Multilingual and Cross-lingual Word-in-Context Disambiguation","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ta-mamc-at-semeval-2021-task-4-task-adaptive","title":"TA-MAMC at SemEval-2021 Task 4: Task-adaptive Pretraining and Multi-head Attention for Abstract Meaning Reading Comprehension","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tsia-at-semeval-2021-task-7-detecting-and","title":"Tsia at SemEval-2021 Task 7: Detecting and Rating Humor and Offense","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ualberta-at-semeval-2021-task-2-determining","title":"UAlberta at SemEval-2021 Task 2: Determining Sense Synonymy via Translations","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-paradigm-clustering-using","title":"Unsupervised Paradigm Clustering Using Transformation Rules","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uob-uk-at-semeval-2021-task-2-zero-shot-and","title":"UoB\\_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"icdar-2021-competition-on-scene-video-text","title":"ICDAR 2021 Competition on Scene Video Text Spotting","date":"2021-07-26","arxiv_id":"2107.11919","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-supervised-models-and-learned","title":"Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases","date":"2021-07-08","arxiv_id":"2107.03985","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-user-voicefilter-lite-via-attentive","title":"Multi-user VoiceFilter-Lite via Attentive Speaker Embedding","date":"2021-07-02","arxiv_id":"2107.01201","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-does-bert-process-disfluency","title":"How does BERT process disfluency?","date":"2021-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adventurer-s-treasure-hunt-a-transparent","title":"Adventurer's Treasure Hunt: A Transparent System for Visually Grounded Compositional Visual Question Answering based on Scene Graphs","date":"2021-06-28","arxiv_id":"2106.14476","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-sexism-detection-with-multilingual","title":"Automatic Sexism Detection with Multilingual Transformer Models","date":"2021-06-09","arxiv_id":"2106.04908","repositories_listed":0,"syntology":null},{"url":null,"slug":"kfu-nlp-team-at-smm4h-2021-tasks-cross","title":"KFU NLP Team at SMM4H 2021 Tasks: Cross-lingual and Cross-modal BERT-based Models for Adverse Drug Effects","date":"2021-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semeval-2021-task-1-lexical-complexity","title":"SemEval-2021 Task 1: Lexical Complexity Prediction","date":"2021-06-01","arxiv_id":"2106.00473","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-math-kcs-via-task-adaptive-pre","title":"Classifying Math KCs via Task-Adaptive Pre-Trained BERT","date":"2021-05-24","arxiv_id":"2105.11343","repositories_listed":0,"syntology":null},{"url":null,"slug":"iitp-at-aila-2019-system-report-for","title":"IITP at AILA 2019: System Report for Artificial Intelligence for Legal Assistance Shared Task","date":"2021-05-24","arxiv_id":"2105.11347","repositories_listed":0,"syntology":null},{"url":null,"slug":"pali-at-semeval-2021-task-2-fine-tune-xlm","title":"PALI at SemEval-2021 Task 2: Fine-Tune XLM-RoBERTa for Word in Context Disambiguation","date":"2021-04-21","arxiv_id":"2104.10375","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-analysis-of-indexing-and-querying","title":"An Analysis of Indexing and Querying Strategies on a Technologically Assisted Review Task","date":"2021-04-20","arxiv_id":"2104.09774","repositories_listed":0,"syntology":null},{"url":null,"slug":"lu-bzu-at-semeval-2021-task-2-word2vec-and","title":"LU-BZU at SemEval-2021 Task 2: Word2Vec and Lemma2Vec performance in Arabic Word-in-Context disambiguation","date":"2021-04-16","arxiv_id":"2104.08110","repositories_listed":0,"syntology":null},{"url":null,"slug":"gridtopix-training-embodied-agents-with","title":"GridToPix: Training Embodied Agents with Minimal Supervision","date":"2021-04-14","arxiv_id":"2105.00931","repositories_listed":0,"syntology":null},{"url":null,"slug":"transwic-at-semeval-2021-task-2-transformer","title":"TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation","date":"2021-04-09","arxiv_id":"2104.04632","repositories_listed":0,"syntology":null},{"url":null,"slug":"uppsala-nlp-at-semeval-2021-task-2","title":"Uppsala NLP at SemEval-2021 Task 2: Multilingual Language Models for Fine-tuning and Feature Extraction in Word-in-Context Disambiguation","date":"2021-04-08","arxiv_id":"2104.03767","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-frequency-translational","title":"Integrating Frequency Translational Invariance in TDNNs and Frequency Positional Information in 2D ResNets to Enhance Speaker Verification","date":"2021-04-06","arxiv_id":"2104.02370","repositories_listed":0,"syntology":null},{"url":null,"slug":"mcl-iitk-at-semeval-2021-task-2-multilingual","title":"MCL@IITK at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation using Augmented Data, Signals, and Transformers","date":"2021-04-04","arxiv_id":"2104.01567","repositories_listed":0,"syntology":null},{"url":null,"slug":"autobots-lt-edi-eacl2021-one-world-one-family","title":"Autobots@LT-EDI-EACL2021: One World, One Family: Hope Speech Detection with BERT Transformer Model","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deepblueai-at-wanlp-eacl2021-task-2-a-deep","title":"DeepBlueAI at WANLP-EACL2021 task 2: A Deep Ensemble-based Method for Sarcasm and Sentiment Detection in Arabic","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ia-gcn-interpretable-attention-based-graph","title":"IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction","date":"2021-03-29","arxiv_id":"2103.15587","repositories_listed":0,"syntology":null},{"url":null,"slug":"arabert-and-farasa-segmentation-based","title":"AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets","date":"2021-03-02","arxiv_id":"2103.01679","repositories_listed":0,"syntology":null},{"url":null,"slug":"bert-based-acronym-disambiguation-with","title":"BERT-based Acronym Disambiguation with Multiple Training Strategies","date":"2021-02-25","arxiv_id":"2103.00488","repositories_listed":0,"syntology":null},{"url":null,"slug":"kbcnmujal-hasoc-dravidian-codemix-fire2020","title":"KBCNMUJAL@HASOC-Dravidian-CodeMix-FIRE2020: Using Machine Learning for Detection of Hate Speech and Offensive Code-Mixed Social Media text","date":"2021-02-19","arxiv_id":"2102.09866","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-and-analysis-on-automated-glioma","title":"A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction","date":"2021-01-26","arxiv_id":"2101.10599","repositories_listed":0,"syntology":null},{"url":null,"slug":"wechat-ai-s-submission-for-dstc9-interactive","title":"WeChat AI & ICT's Submission for DSTC9 Interactive Dialogue Evaluation Track","date":"2021-01-20","arxiv_id":"2101.07947","repositories_listed":0,"syntology":null},{"url":"/paper/hand-pose-estimation-in-the-task-of","slug":"hand-pose-estimation-in-the-task-of","title":"Hand Pose Estimation in the Task of Egocentric Actions","date":"2021-01-11","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"thuir-coliee-2020-leveraging-semantic","title":"THUIR@COLIEE-2020: Leveraging Semantic Understanding and Exact Matching for Legal Case Retrieval and Entailment","date":"2020-12-24","arxiv_id":"2012.13102","repositories_listed":0,"syntology":null},{"url":"/paper/seeing-past-words-testing-the-cross-modal","slug":"seeing-past-words-testing-the-cross-modal","title":"Seeing past words: Testing the cross-modal capabilities of pretrained V&L models on counting tasks","date":"2020-12-22","arxiv_id":"2012.12352","repositories_listed":0,"syntology":null},{"url":null,"slug":"dartmouth-cs-at-wnut-2020-task-2-informative","title":"Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet Classification Using BERT","date":"2020-12-07","arxiv_id":"2012.04539","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-offensive-language-detection-through","title":"Enhanced Offensive Language Detection Through Data Augmentation","date":"2020-12-05","arxiv_id":"2012.02954","repositories_listed":0,"syntology":null},{"url":null,"slug":"financial-document-causality-detection-shared","title":"Financial Document Causality Detection Shared Task (FinCausal 2020)","date":"2020-12-04","arxiv_id":"2012.02505","repositories_listed":0,"syntology":null},{"url":null,"slug":"adverse-drug-reaction-detection-in-twitter","title":"Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"amobee-at-semeval-2020-task-7-regularization","title":"Amobee at SemEval-2020 Task 7: Regularization of Language Model Based Classifiers","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"approaching-smm4h-2020-with-ensembles-of-bert","title":"Approaching SMM4H 2020 with Ensembles of BERT Flavours","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"autobots-ensemble-identifying-and-extracting","title":"Autobots Ensemble: Identifying and Extracting Adverse Drug Reaction from Tweets Using Transformer Based Pipelines","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bmeaut-at-semeval-2020-task-2-lexical","title":"BMEAUT at SemEval-2020 Task 2: Lexical Entailment with Semantic Graphs","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fraunhofer-iais-at-fincausal-2020-tasks-1-2","title":"Fraunhofer IAIS at FinCausal 2020, Tasks 1 & 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hit-scir-at-semeval-2020-task-5-training-pre","title":"HIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hitsz-icrc-a-report-for-smm4h-shared-task-1","title":"HITSZ-ICRC: A Report for SMM4H Shared Task 2020-Automatic Classification of Medications and Adverse Effect in Tweets","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-far-can-we-go-with-just-out-of-the-box","title":"How Far Can We Go with Just Out-of-the-box BERT Models?","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-medication-abuse-and-adverse","title":"Identifying Medication Abuse and Adverse Effects from Tweets: University of Michigan at #SMM4H 2020","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"jdd-fincausal-2020-task-2-financial-document","title":"JDD @ FinCausal 2020, Task 2: Financial Document Causality Detection","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"litl-at-smm4h-an-old-school-feature-based","title":"LITL at SMM4H: An Old-school Feature-based Classifier for Identifying Adverse Effects in Tweets","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lmml-at-semeval-2020-task-7-siamese","title":"LMML at SemEval-2020 Task 7: Siamese Transformers for Rating Humor in Edited News Headlines","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semeval-2020-task-2-predicting-multilingual","title":"SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-classification-with-imbalanced-data","title":"Sentence Classification with Imbalanced Data for Health Applications","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-transformers-and-bayesian","title":"Sentence Transformers and Bayesian Optimization for Adverse Drug Effect Detection from Twitter","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"shikeblcu-at-semeval-2020-task-2-an-external","title":"SHIKEBLCU at SemEval-2020 Task 2: An External Knowledge-enhanced Matrix for Multilingual and Cross-Lingual Lexical Entailment","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"so-at-semeval-2020-task-7-deeppavlov-logistic","title":"SO at SemEval-2020 Task 7: DeepPavlov Logistic Regression with BERT Embeddings vs SVR at Funniness Evaluation","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-financial-document-causality-detection-1","title":"The Financial Document Causality Detection Shared Task (FinCausal 2020)","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ualberta-at-semeval-2020-task-2-using","title":"UAlberta at SemEval-2020 Task 2: Using Translations to Predict Cross-Lingual Entailment","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"utfpr-at-semeval-2020-task-7-using-co","title":"UTFPR at SemEval-2020 Task 7: Using Co-occurrence Frequencies to Capture Unexpectedness","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"from-protocol-to-screening-a-hybrid-learning","title":"From Protocol to Screening: A Hybrid Learning Approach for Technology-Assisted Systematic Literature Reviews","date":"2020-11-19","arxiv_id":"2011.09752","repositories_listed":0,"syntology":null},{"url":null,"slug":"nit-covid-19-at-wnut-2020-task-2-deep","title":"NIT COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English Tweets","date":"2020-11-11","arxiv_id":"2011.05551","repositories_listed":0,"syntology":null},{"url":null,"slug":"topic-centric-unsupervised-multi-document","title":"Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles","date":"2020-11-03","arxiv_id":"2011.08072","repositories_listed":0,"syntology":null},{"url":null,"slug":"bergamot-latte-submissions-for-the-wmt20","title":"BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"complexdatalab-at-w-nut-2020-task-2-detecting","title":"ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"csecu-dsg-at-wnut-2020-task-2-exploiting","title":"CSECU-DSG at WNUT-2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative COVID-19 English Tweets","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cxp949-at-wnut-2020-task-2-extracting-1","title":"CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets - RoBERTa Ensembles and The Continued Relevance of Handcrafted Features","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dsc-iit-ism-at-wnut-2020-task-2-detection-of","title":"DSC-IIT ISM at WNUT-2020 Task 2: Detection of COVID-19 informative tweets using RoBERTa","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"emory-at-wnut-2020-task-2-combining","title":"Emory at WNUT-2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gcdh-at-wnut-2020-task-2-bert-based-models","title":"#GCDH at WNUT-2020 Task 2: BERT-Based Models for the Detection of Informativeness in English COVID-19 Related Tweets","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iiitbh-at-wnut-2020-task-2-exploiting-the","title":"IIITBH at WNUT-2020 Task 2: Exploiting the best of both worlds","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iswara-at-wnut-2020-task-2-identification-of","title":"ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"linguist-geeks-on-wnut-2020-task-2-covid-19","title":"Linguist Geeks on WNUT-2020 Task 2: COVID-19 Informative Tweet Identification using Progressive Trained Language Models and Data Augmentation","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nhk-strl-at-wnut-2020-task-2-gats-with","title":"NHK_STRL at WNUT-2020 Task 2: GATs with Syntactic Dependencies as Edges and CTC-based Loss for Text Classification","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"61a09017dcb6411b232ad65e9b96180e6198dcf6bee5f1d9dfeab4a0650b4a51","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}