Table of Contents

• Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider 0001, Siddharth Singh, Shyam Ratan. [doi]
• Semeval-2022 Task 1: CODWOE - Comparing Dictionaries and Word EmbeddingsTimothee Mickus, Kees van Deemter, Mathieu Constant, Denis Paperno. 1-14 [doi]
• 1Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse Dictionary TaskZhiyong Wang, Ge Zhang, Nineli Lashkarashvili. 15-22 [doi]
• BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition ModelingCunliang Kong, Yujie Wang, Ruining Chong, Liner Yang, Hengyuan Zhang, Erhong Yang, Yaping Huang. 23-28 [doi]
• LingJing at SemEval-2022 Task 1: Multi-task Self-supervised Pre-training for Multilingual Reverse DictionaryBin Li, Yixuan Weng, Fei Xia, Shizhu He, Bin Sun 0001, Shutao Li. 29-35 [doi]
• IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic RepresentationsDamir Korencic, Ivan Grubisic. 36-59 [doi]
• TLDR at SemEval-2022 Task 1: Using Transformers to Learn Dictionaries and RepresentationsAditya Srivastava, Harsha Vardhan Vemulapati. 60-67 [doi]
• MMG at SemEval-2022 Task 1: A Reverse Dictionary approach based on a review of the dataset from a lexicographic perspectiveAlfonso Ardoiz, Miguel Ortega-Martín, Óscar García-Sierra, Jorge Álvarez, Ignacio Arranz, Adrián Alonso. 68-74 [doi]
• Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and DefinitionsPinzhen Chen, Zheng Zhao. 75-81 [doi]
• RIGA at SemEval-2022 Task 1: Scaling Recurrent Neural Networks for CODWOE Dictionary ModelingEduards Mukans, Gus Strazds, Guntis Barzdins. 82-87 [doi]
• Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary?Rafal Cerniavski, Sara Stymne. 88-93 [doi]
• BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencodersNihed Bendahman, Julien Breton, Lina Nicolaieff, Mokhtar Boumedyen Billami, Christophe Bortolaso, Youssef Miloudi. 94-100 [doi]
• JSI at SemEval-2022 Task 1: CODWOE - Reverse Dictionary: Monolingual and cross-lingual approachesTran Thi Hong Hanh, Matej Martinc, Matthew Purver, Senja Pollak. 101-106 [doi]
• SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence EmbeddingHarish Tayyar Madabushi, Edward Gow-Smith, Marcos García 0001, Carolina Scarton, Marco Idiart, Aline Villavicencio. 107-121 [doi]
• Helsinki-NLP at SemEval-2022 Task 2: A Feature-Based Approach to Multilingual Idiomaticity DetectionSami Itkonen, Jörg Tiedemann, Mathias Creutz. 122-134 [doi]
• Hitachi at SemEval-2022 Task 2: On the Effectiveness of Span-based Classification Approaches for Multilingual Idiomaticity DetectionAtsuki Yamaguchi, Gaku Morio, Hiroaki Ozaki, Yasuhiro Sogawa. 135-144 [doi]
• UAlberta at SemEval 2022 Task 2: Leveraging Glosses and Translations for Multilingual Idiomaticity DetectionBradley Hauer, Seeratpal Jaura, Talgat Omarov, Grzegorz Kondrak. 145-150 [doi]
• HYU at SemEval-2022 Task 2: Effective Idiomaticity Detection with Consideration at Different Levels of ContextualizationYoungju Joung, Taeuk Kim. 151-157 [doi]
• drsphelps at SemEval-2022 Task 2: Learning idiom representations using BERTRAMDylan Phelps. 158-164 [doi]
• JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot LearningYash Jakhotiya, Vaibhav Kumar, Ashwin Pathak, Raj Shah. 165-168 [doi]
• CardiffNLP-Metaphor at SemEval-2022 Task 2: Targeted Fine-tuning of Transformer-based Language Models for Idiomaticity DetectionJoanne Boisson, José Camacho-Collados, Luis Espinosa Anke. 169-177 [doi]
• kpfriends at SemEval-2022 Task 2: NEAMER - Named Entity Augmented Multi-word Expression RecognizerMin Sik Oh. 178-185 [doi]
• daminglu123 at SemEval-2022 Task 2: Using BERT and LSTM to Do Text ClassificationDaming Lu. 186-189 [doi]
• HiJoNLP at SemEval-2022 Task 2: Detecting Idiomaticity of Multiword Expressions using Multilingual Pretrained Language ModelsMinghuan Tan. 190-196 [doi]
• ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword RepresentationsXuange Cui, Wei Xiong, Songlin Wang. 197-203 [doi]
• NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity DetectionSimone Tedeschi, Roberto Navigli. 204-210 [doi]
• YNU-HPCC at SemEval-2022 Task 2: Representing Multilingual Idiomaticity based on Contrastive LearningKuanghong Liu, Jin Wang 0008, Xuejie Zhang 0002. 211-216 [doi]
• OCHADAI at SemEval-2022 Task 2: Adversarial Training for Multilingual Idiomaticity DetectionLis Pereira, Ichiro Kobayashi. 217-220 [doi]
• HIT at SemEval-2022 Task 2: Pre-trained Language Model for Idioms DetectionZheng Chu, Ziqing Yang 0001, Yiming Cui, Zhigang Chen 0003, Ming Liu. 221-227 [doi]
• SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic KnowledgeRoberto Zamparelli, Shammur A. Chowdhury, Dominique Brunato, Cristiano Chesi, Felice dell'Orletta, Md. Arid Hasan, Giulia Venturi. 228-238 [doi]
• LingJing at SemEval-2022 Task 3: Applying DeBERTa to Lexical-level Presupposed Relation Taxonomy with Knowledge TransferFei Xia, Bin Li, Yixuan Weng, Shizhu He, Bin Sun 0001, Shutao Li, Kang Liu 0001, Jun Zhao 0001. 239-246 [doi]
• RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word RelationsWessel Poelman, Gijs Danoe, Esther Ploeger, Frank van den Berg, Tommaso Caselli, Lukas Edman. 247-254 [doi]
• CSECU-DSG at SemEval-2022 Task 3: Investigating the Taxonomic Relationship Between Two Arguments using Fusion of Multilingual Transformer ModelsAbdul Aziz, Md. Akram Hossain, Abu Nowshed Chy. 255-259 [doi]
• UoR-NCL at SemEval-2022 Task 3: Fine-Tuning the BERT-Based Models for Validating Taxonomic RelationsThanet Markchom, Huizhi Liang, Jiaoyan Chen. 260-265 [doi]
• SPDB Innovation Lab at SemEval-2022 Task 3: Recognize Appropriate Taxonomic Relations Between Two Nominal Arguments with ERNIE-M ModelYue Zhou, Bowei Wei, Jianyu Liu, Yang Yang. 266-270 [doi]
• UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentationInjy Sarhan, Pablo Mosteiro, Marco Spruit. 271-281 [doi]
• KaMiKla at SemEval-2022 Task 3: AlBERTo, BERT, and CamemBERT - Be(r)tween Taxonomy Detection and PredictionKarl Vetter, Miriam Segiet, Klara Lennermann. 282-290 [doi]
• HW-TSC at SemEval-2022 Task 3: A Unified Approach Fine-tuned on Multilingual Pretrained Model for PreTENSYinglu Li, Min Zhang, Xiaosong Qiao, Minghan Wang. 291-297 [doi]
• SemEval-2022 Task 4: Patronizing and Condescending Language DetectionCarla Pérez-Almendros, Luis Espinosa Anke, Steven Schockaert. 298-307 [doi]
• JUST-DEEP at SemEval-2022 Task 4: Using Deep Learning Techniques to Reveal Patronizing and Condescending LanguageMohammad Makahleh, Naba Bani Yaseen, Malak Abdullah. 308-312 [doi]
• PINGAN Omini-Sinitic at SemEval-2022 Task 4: Multi-prompt Training for Patronizing and Condescending Language DetectionYe Wang, Yanmeng Wang, Baishun Ling, Zexiang Liao, Shaojun Wang, Jing Xiao 0006. 313-318 [doi]
• BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language DetectionYong Deng, Chenxiao Dou, Liangyu Chen, Deqiang Miao, Xianghui Sun, Baochang Ma, Xiangang Li. 319-323 [doi]
• DH-FBK at SemEval-2022 Task 4: Leveraging Annotators' Disagreement and Multiple Data Views for Patronizing Language DetectionAlan Ramponi, Elisa Leonardelli. 324-334 [doi]
• PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language DetectionDou Hu 0001, Mengyuan Zhou, Xiyang Du, Mengfei Yuan, Jin Zhi, Lian-Xin Jiang, Yang Mo, Xiaofeng Shi. 335-343 [doi]
• ASRtrans at SemEval-2022 Task 4: Ensemble of Tuned Transformer-based Models for PCL DetectionAilneni Rakshitha Rao. 344-351 [doi]
• LastResort at SemEval-2022 Task 4: Towards Patronizing and Condescending Language Detection using Pre-trained Transformer Based Models EnsemblesSamyak Agrawal, Radhika Mamidi. 352-356 [doi]
• Felix&Julia at SemEval-2022 Task 4: Patronizing and Condescending Language DetectionFelix Herrmann, Julia Krebs. 357-362 [doi]
• MS@IW at SemEval-2022 Task 4: Patronising and Condescending Language Detection with Synthetically Generated DataSelina Meyer, Maximilian Schmidhuber, Udo Kruschwitz. 363-368 [doi]
• Team LEGO at SemEval-2022 Task 4: Machine Learning Methods for PCL DetectionAbhishek Singh. 369-373 [doi]
• RNRE-NLP at SemEval-2022 Task 4: Patronizing and Condescending Language DetectionRylan Yang, Ethan Chi, Nathan Chi. 374-378 [doi]
• UTSA NLP at SemEval-2022 Task 4: An Exploration of Simple Ensembles of Transformers, Convolutional, and Recurrent Neural NetworksXingmeng Zhao, Anthony Rios. 379-386 [doi]
• AliEdalat at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Fine-tuned Language Models, BERT+BiGRU, and Ensemble ModelsAli Edalat, Yadollah Yaghoobzadeh, Behnam Bahrak. 387-393 [doi]
• Tesla at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Transformer-based Models with Data AugmentationSahil Bhatt, Manish Shrivastava 0001. 394-399 [doi]
• SSN_NLP_MLRG at SemEval-2022 Task 4: Ensemble Learning strategies to detect Patronizing and Condescending LanguageKalaivani Adaikkan, Thenmozhi Durairaj. 400-404 [doi]
• Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule NetworksSihui Li, Xiaobing Zhou. 405-408 [doi]
• McRock at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Multi-Channel CNN, Hybrid LSTM, DistilBERT and XLNetMarco Siino, Marco La Cascia, Ilenia Tinnirello. 409-417 [doi]
• Team Stanford ACMLab at SemEval 2022 Task 4: Textual Analysis of PCL Using Contextual Word EmbeddingsUpamanyu Dass-Vattam, Spencer Wallace, Rohan Sikand, Zach Witzel, Jillian Tang. 418-420 [doi]
• Team LRL_NC at SemEval-2022 Task 4: Binary and Multi-label Classification of PCL using Fine-tuned Transformer-based ModelsKushagri Tandon, Niladri Chatterjee. 421-431 [doi]
• GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language DetectionJunyu Lu, Hao Zhang, Tongyue Zhang, Hongbo Wang, Haohao Zhu, Bo Xu, Hongfei Lin. 432-437 [doi]
• HITMI&T at SemEval-2022 Task 4: Investigating Task-Adaptive Pretraining And Attention Mechanism On PCL DetectionZihang Liu, Yancheng He, Feiqing Zhuang, Bing Xu. 438-444 [doi]
• UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending LanguageDavid Koleczek, Alexander Scarlatos, Preshma Linet Pereira, Siddha Makarand Karkare. 445-453 [doi]
• YNU-HPCC at SemEval-2022 Task 4: Finetuning Pretrained Language Models for Patronizing and Condescending Language DetectionWenqiang Bai, Jin Wang, Xuejie Zhang. 454-458 [doi]
• I2C at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Deep Learning TechniquesLaura Vázquez Ramos, Adrián Moreno Monterde, Victoria Pachón, Jacinto Mata. 459-463 [doi]
• PiCkLe at SemEval-2022 Task 4: Boosting Pre-trained Language Models with Task Specific Metadata and Cost Sensitive LearningManan Suri. 464-472 [doi]
• ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and Condescending LanguageTosin P. Adewumi, Lama Alkhaled, Hamam Mokayed, Foteini Liwicki, Marcus Liwicki. 473-478 [doi]
• Xu at SemEval-2022 Task 4: Pre-BERT Neural Network Methods vs Post-BERT RoBERTa Approach for Patronizing and Condescending Language DetectionJinghua Xu. 479-484 [doi]
• Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL DetectionAlejandro Mosquera. 485-489 [doi]
• SATLab at SemEval-2022 Task 4: Trying to Detect Patronizing and Condescending Language with only Character and Word N-gramsYves Bestgen. 490-495 [doi]
• Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending LanguageJayant Chhillar. 496-502 [doi]
• CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3Daniel Saeedi, Sirwe Saeedi, Aliakbar Panahi, Alvis Cheuk M. Fong. 503-508 [doi]
• University of Bucharest Team at Semeval-2022 Task4: Detection and Classification of Patronizing and Condescending LanguageTudor Dumitrascu, Raluca-Andreea Gînga, Bogdan Dobre, Bogdan Radu Silviu Sielecki. 509-514 [doi]
• Amrita_CEN at SemEval-2022 Task 4: Oversampling-based Machine Learning Approach for Detecting Patronizing and Condescending LanguageBichu George, Adarsh S, Nishitkumar Prajapati, Premjith B, Soman KP. 515-518 [doi]
• JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Preprocessing Methods and various Machine Leaerning MethodsYaakov HaCohen-Kerner, Ilan Meyrowitsch, Matan Fchima. 519-524 [doi]
• ULFRI at SemEval-2022 Task 4: Leveraging uncertainty and additional knowledge for patronizing and condescending language detectionMatej Klemen, Marko Robnik-Sikonja. 525-532 [doi]
• SemEval-2022 Task 5: Multimedia Automatic Misogyny IdentificationElisabetta Fersini, Francesca Gasparini, Giulia Rizzi, Aurora Saibene, Berta Chulvi, Paolo Rosso, Alyssa Lees, Jeffrey Sorensen. 533-549 [doi]
• Transformers at SemEval-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme DetectionShankar Mahadevan, Sean Benhur, Roshan Nayak, Malliga Subramanian, Kogilavani Shanmugavadivel, Kanchana Sivanraju, Bharathi Raja Chakravarthi. 550-554 [doi]
• PAIC at SemEval-2022 Task 5: Multi-Modal Misogynous Detection in MEMES with Multi-Task Learning And Multi-model FusionJin Zhi, Mengyuan Zhou, Mengfei Yuan, Dou Hu 0001, Xiyang Du, Lian-Xin Jiang, Yang Mo, Xiaofeng Shi. 555-562 [doi]
• DD-TIG at SemEval-2022 Task 5: Investigating the Relationships Between Multimodal and Unimodal Information in Misogynous Memes Detection and ClassificationZiming Zhou, Han Zhao, Jingjing Dong, Ning Ding, Xiaolong Liu, Kangli Zhang. 563-570 [doi]
• TechSSN at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification using Deep Learning ModelsRajalakshmi Sivanaiah, Angel Deborah S, Sakaya Milton Rajendram, T. T. Mirnalinee. 571-574 [doi]
• LastResort at SemEval-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific PretrainingSamyak Agrawal, Radhika Mamidi. 575-580 [doi]
• HateU at SemEval-2022 Task 5: Multimedia Automatic Misogyny IdentificationAyme Arango, Jesus Perez-Martin, Arniel Labrada. 581-584 [doi]
• SRCB at SemEval-2022 Task 5: Pretraining Based Image to Text Late Sequential Fusion System for Multimodal Misogynous Meme IdentificationJing Zhang, Yujin Wang. 585-596 [doi]
• ASRtrans at SemEval-2022 Task 5: Transformer-based Models for Meme ClassificationAilneni Rakshitha Rao, Arjun Rao. 597-604 [doi]
• UAEM-ITAM at SemEval-2022 Task 5: Vision-Language Approach to Recognize Misogynous Content in MemesEdgar Roman-Rangel, Jorge Fuentes-Pacheco, Jorge Hermosillo Valadez. 605-609 [doi]
• JRLV at SemEval-2022 Task 5: The Importance of Visual Elements for Misogyny Identification in MemesJason Ravagli, Lorenzo Vaiani. 610-617 [doi]
• UPB at SemEval-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny IdentificationAndrei Paraschiv, Mihai Dascalu, Dumitru-Clementin Cercel. 618-625 [doi]
• RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEsWentao Yu, Benedikt T. Boenninghoff, Jonas Roehrig, Dorothea Kolossa. 626-635 [doi]
• RIT Boston at SemEval-2022 Task 5: Multimedia Misogyny Detection By Using Coherent Visual and Language Features from CLIP Model and Data-centric AI PrincipleLei Chen, Hou Wei Chou. 636-641 [doi]
• TeamOtter at SemEval-2022 Task 5: Detecting Misogynistic Content in Multimodal MemesParidhi Maheshwari, Sharmila Reddy Nangi. 642-647 [doi]
• taochen at SemEval-2022 Task 5: Multimodal Multitask Learning and Ensemble LearningChen Tao, Jung-jae Kim. 648-653 [doi]
• MilaNLP at SemEval-2022 Task 5: Using Perceiver IO for Detecting Misogynous Memes with Text and Image ModalitiesGiuseppe Attanasio, Debora Nozza, Federico Bianchi. 654-662 [doi]
• UniBO at SemEval-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in MemesArianna Muti, Katerina Korre, Alberto Barrón-Cedeño. 663-672 [doi]
• IIITH at SemEval-2022 Task 5: A comparative study of deep learning models for identifying misogynous memesTathagata Raha, Sagar Joshi, Vasudeva Varma. 673-678 [doi]
• Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification FrameworkAhmed Mahran, Carlo Alessandro Borella, Konstantinos Perifanos. 679-688 [doi]
• I2C at SemEval-2022 Task 5: Identification of misogyny in internet memesPablo Cordon, Pablo Gonzalez Diaz, Jacinto Mata, Victoria Pachón. 689-694 [doi]
• INF-UFRGS at SemEval-2022 Task 5: analyzing the performance of multimodal modelsGustavo Acauan Lorentz, Viviane P. Moreira. 695-699 [doi]
• MMVAE at SemEval-2022 Task 5: A Multi-modal Multi-task VAE on Misogynous Meme DetectionYimeng Gu, Ignacio Castro, Gareth Tyson. 700-710 [doi]
• AMS_ADRN at SemEval-2022 Task 5: A Suitable Image-text Multimodal Joint Modeling Method for Multi-task Misogyny IdentificationDa Li, Ming Yi, Yukai He. 711-717 [doi]
• University of Hildesheim at SemEval-2022 task 5: Combining Deep Text and Image Models for Multimedia Misogyny DetectionMilan Kalkenings, Thomas Mandl 0001. 718-723 [doi]
• Mitra Behzadi at SemEval-2022 Task 5 : Multimedia Automatic Misogyny Identification method based on CLIPMitra Behzadi, Ali Derakhshan, Ian G. Harris. 724-727 [doi]
• IITR CodeBusters at SemEval-2022 Task 5: Misogyny Identification using TransformersGagan Sharma, Gajanan Sunil Gitte, Shlok Goyal, Raksha Sharma. 728-732 [doi]
• IIT DHANBAD CODECHAMPS at SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny IdentificationShubham Barnwal, Ritesh Kumar, Rajendra Pamula. 733-735 [doi]
• QiNiAn at SemEval-2022 Task 5: Multi-Modal Misogyny Detection and ClassificationQin Gu, Nino Meisinger, Anna-Katharina Dick. 736-741 [doi]
• UMUTeam at SemEval-2022 Task 5: Combining image and textual embeddings for multi-modal automatic misogyny identificationJosé Antonio García-Díaz, Camilo Caparrós-Laiz, Rafael Valencia-García. 742-747 [doi]
• YNU-HPCC at SemEval-2022 Task 5: Multi-Modal and Multi-label Emotion Classification Based on LXMERTChao Han, Jin Wang, Xuejie Zhang. 748-755 [doi]
• TIB-VA at SemEval-2022 Task 5: A Multimodal Architecture for the Detection and Classification of Misogynous MemesSherzod Hakimov, Gullal Singh Cheema, Ralph Ewerth. 756-760 [doi]
• R2D2 at SemEval-2022 Task 5: Attention is only as good as its Values! A multimodal system for identifying misogynist memesMayukh Sharma, Ilanthenral Kandasamy, W. B. Vasantha 0001. 761-770 [doi]
• AIDA-UPM at SemEval-2022 Task 5: Exploring Multimodal Late Information Fusion for Multimedia Automatic Misogyny IdentificationÁlvaro Huertas-García, Helena Liz, Guillermo Villar-Rodríguez, Alejandro Martín, Javier Huertas-Tato, David Camacho. 771-779 [doi]
• YMAI at SemEval-2022 Task 5: Detecting Misogyny in Memes using VisualBERT and MMBT MultiModal Pre-trained ModelsMohammad Habash, Yahya Daqour, Malak Abdullah, Mahmoud Al-Ayyoub. 780-784 [doi]
• Exploring Contrastive Learning for Multimodal Detection of Misogynistic MemesCharic Farinango Cuervo, Natalie Parde. 785-792 [doi]
• Poirot at SemEval-2022 Task 5: Leveraging Graph Network for Misogynistic Meme DetectionHarshvardhan Srivastava. 793-801 [doi]
• SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and ArabicIbrahim Abu Farha, Silviu Vlad Oprea, Steven R. Wilson 0001, Walid Magdy. 802-814 [doi]
• PALI-NLP at SemEval-2022 Task 6: iSarcasmEval- Fine-tuning the Pre-trained Model for Detecting Intended SarcasmXiyang Du, Dou Hu 0001, Jin Zhi, Lian-Xin Jiang, Xiaofeng Shi. 815-819 [doi]
• stce at SemEval-2022 Task 6: Sarcasm Detection in English TweetsMengfei Yuan, Mengyuan Zhou, Lian-Xin Jiang, Yang Mo, Xiaofeng Shi. 820-826 [doi]
• GetSmartMSEC at SemEval-2022 Task 6: Sarcasm Detection using Contextual Word Embedding with Gaussian model for Irony Type IdentificationDiksha Krishnan, Jerin Mahibha C, Thenmozhi Durairaj. 827-833 [doi]
• Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using OversamplingAparna K. Ajayan, Krishna Mohanan, Anugraha S, Premjith B, Soman KP. 834-839 [doi]
• High Tech team at SemEval-2022 Task 6: Intended Sarcasm Detection for Arabic textsHamza Alami, Abdessamad Benlahbib, Ahmed Alami. 840-843 [doi]
• CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and ArabicAbdelkader El Mahdaouy, Abdellah El Mekki, Kabil Essefar, Abderrahman Skiredj, Ismail Berrada. 844-850 [doi]
• TechSSN at SemEval-2022 Task 6: Intended Sarcasm Detection using Transformer ModelsRamdhanush V, Rajalakshmi Sivanaiah, Angel Deborah S, Sakaya Milton Rajendram, T. T. Mirnalinee. 851-855 [doi]
• I2C at SemEval-2022 Task 6: Intended Sarcasm in English using Deep Learning TechniquesAdrián Moreno Monterde, Laura Vázquez Ramos, Jacinto Mata, Victoria Pachón Álvarez. 856-861 [doi]
• NULL at SemEval-2022 Task 6: Intended Sarcasm Detection Using Stylistically Fused Contextualized Representation and Deep LearningMostafa Rahgouy, Hamed Babaei Giglou, Taher Rahgooy, Cheryl Seals. 862-870 [doi]
• UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detectionEmmanuel Osei-Brefo, Huizhi Liang. 871-876 [doi]
• I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep LearningPablo Gonzalez Diaz, Pablo Cordon, Jacinto Mata, Victoria Pachón. 877-880 [doi]
• BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic TextsNsrin Ashraf, Fathy Elkazzaz, Mohamed Taha, Hamada A. Nayel, Tarek Elshishtawy. 881-884 [doi]
• akaBERT at SemEval-2022 Task 6: An Ensemble Transformer-based Model for Arabic Sarcasm DetectionAbdulrahman Mohamed Kamr, Ensaf Mohamed. 885-890 [doi]
• AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning TechniquesAya Lotfy, Marwan Torki, Nagwa M. El-Makky. 891-895 [doi]
• reamtchka at SemEval-2022 Task 6: Investigating the effect of different loss functions for Sarcasm detection for unbalanced datasetsReem Abdel-Salam. 896-906 [doi]
• niksss at SemEval-2022 Task 6: Are Traditionally Pre-Trained Contextual Embeddings Enough for Detecting Intended Sarcasm ?Nikhil Singh 0007. 907-911 [doi]
• Dartmouth at SemEval-2022 Task 6: Detection of SarcasmRishik Lad, Weicheng Ma, Soroush Vosoughi. 912-918 [doi]
• ISD at SemEval-2022 Task 6: Sarcasm Detection Using Lightweight ModelsSamantha Huang, Ethan Chi, Nathan Chi. 919-922 [doi]
• Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data AugmentationMosab Shaheen, Shubham Kumar Nigam. 923-937 [doi]
• IISERB Brains at SemEval-2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in EnglishTanuj Singh Shekhawat, Manoj Kumar, Udaybhan Rathore, Aditya Joshi, Jasabanta Patro. 938-944 [doi]
• connotation_clashers at SemEval-2022 Task 6: The effect of sentiment analysis on sarcasm detectionPatrick Hantsch, Nadav Chkroun. 945-950 [doi]
• TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm DetectionJason Angel, Segun Taofeek Aroyehun, Alexander F. Gelbukh. 951-955 [doi]
• YNU-HPCC at SemEval-2022 Task 6: Transformer-based Model for Intended Sarcasm Detection in English and ArabicGuangmin Zheng, Jin Wang 0008, Xuejie Zhang 0002. 956-961 [doi]
• UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection using generative-based and mutation-based data augmentationAmirhossein Abaskohi, Arash Rasouli, Tanin Zeraati, Behnam Bahrak. 962-969 [doi]
• FII UAIC at SemEval-2022 Task 6: iSarcasmEval - Intended Sarcasm Detection in English and ArabicTudor Manoleasa, Daniela Gifu, Iustin Sandu. 970-977 [doi]
• MarSan at SemEval-2022 Task 6: iSarcasm Detection via T5 and Sequence LearnersMaryam Najafi, Ehsan Tavan. 978-986 [doi]
• LT3 at SemEval-2022 Task 6: Fuzzy-Rough Nearest Neighbor Classification for Sarcasm DetectionOlha Kaminska, Chris Cornelis, Véronique Hoste. 987-992 [doi]
• LISACTeam at SemEval-2022 Task 6: A Transformer based Approach for Intended Sarcasm Detection in English TweetsAbdessamad Benlahbib, Hamza Alami, Ahmed Alami. 993-998 [doi]
• X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm DetectionYa Han, Yekun Chai, Shuohuan Wang, Yu Sun, Hongyi Huang, Guanghao Chen, Yitong Xu, Yang Yang. 999-1004 [doi]
• DUCS at SemEval-2022 Task 6: Exploring Emojis and Sentiments for Sarcasm DetectionVandita Grover, Hema Banati. 1005-1011 [doi]
• UMUTeam at SemEval-2022 Task 6: Evaluating Transformers for detecting Sarcasm in English and ArabicJosé Antonio García-Díaz, Camilo Caparrós-Laiz, Rafael Valencia-García. 1012-1017 [doi]
• R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasmMayukh Sharma, Ilanthenral Kandasamy, W. B. Vasantha 0001. 1018-1024 [doi]
• SarcasmDet at SemEval-2022 Task 6: Detecting Sarcasm using Pre-trained Transformers in English and Arabic LanguagesMalak Abdullah, Dalya Faraj, Safa Swedat, Jumana Khrais, Mahmoud Al-Ayyoub. 1025-1030 [doi]
• JCT at SemEval-2022 Task 6-A: Sarcasm Detection in Tweets Written in English and Arabic using Preprocessing Methods and Word N-gramsYaakov HaCohen-Kerner, Matan Fchima, Ilan Meyrowitsch. 1031-1038 [doi]
• SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional TextsMichael Roth 0001, Talita Anthonio, Anna Sauer. 1039-1049 [doi]
• JBNU-CCLab at SemEval-2022 Task 7: DeBERTa for Identifying Plausible Clarifications in Instructional TextsDaewook Kang, Sung-Min Lee, Eunhwan Park, Seung-Hoon Na. 1050-1055 [doi]
• HW-TSC at SemEval-2022 Task 7: Ensemble Model Based on Pretrained Models for Identifying Plausible ClarificationsXiaosong Qiao, Yinglu Li, Min Zhang, Minghan Wang, Hao Yang, Shimin Tao, Ying Qin. 1056-1061 [doi]
• DuluthNLP at SemEval-2022 Task 7: Classifying Plausible Alternatives with Pre-trained ELECTRASamuel Akrah, Ted Pedersen. 1062-1066 [doi]
• Stanford MLab at SemEval 2022 Task 7: Tree- and Transformer-Based Methods for Clarification PlausibilityThomas Yim, Junha Lee, Rishi Verma, Scott Hickmann, Annie Zhu, Camron Sallade, Ian Ng, Ryan Chi, Patrick Liu. 1067-1070 [doi]
• Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal RegressionMohammadmahdi Nouriborji, Omid Rohanian, David A. Clifton. 1071-1077 [doi]
• X-PuDu at SemEval-2022 Task 7: A Replaced Token Detection Task Pre-trained Model with Pattern-aware Ensembling for Identifying Plausible ClarificationsJunyuan Shang, Shuohuan Wang, Yu Sun, Yanjun Yu, Yue Zhou, Li Xiang, Guixiu Yang. 1078-1083 [doi]
• PALI at SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional TextsMengyuan Zhou, Dou Hu 0001, Mengfei Yuan, Jin Zhi, Xiyang Du, Lian-Xin Jiang, Yang Mo, Xiaofeng Shi. 1084-1089 [doi]
• niksss at SemEval-2022 Task7: Transformers for Grading the Clarifications on Instructional TextsNikhil Singh 0007. 1090-1093 [doi]
• SemEval-2022 Task 8: Multilingual news article similarityXi Chen, Ali Zeynali, Chico Q. Camargo, Fabian Flöck, Devin Gaffney, Przemyslaw A. Grabowicz, Scott A. Hale, David Jurgens, Mattia Samory. 1094-1106 [doi]
• EMBEDDIA at SemEval-2022 Task 8: Investigating Sentence, Image, and Knowledge Graph Representations for Multilingual News Article SimilarityElaine Zosa, Emanuela Boros, Boshko Koloski, Lidia Pivovarova. 1107-1113 [doi]
• HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News SimilarityZihang Xu, Ziqing Yang 0001, Yiming Cui, Zhigang Chen 0003. 1114-1120 [doi]
• GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article SimilarityIknoor Singh, Yue Li, Melissa Thong, Carolina Scarton. 1121-1128 [doi]
• SemEval-2022 Task 8: Multi-lingual News Article SimilarityNikhil Goel, Ranjith Reddy Bommidi. 1129-1135 [doi]
• SkoltechNLP at SemEval-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector RepresentationsMikhail Kuimov, Daryna Dementieva, Alexander Panchenko. 1136-1144 [doi]
• IIIT-MLNS at SemEval-2022 Task 8: Siamese Architecture for Modeling Multilingual News SimilaritySagar Joshi, Dhaval Taunk, Vasudeva Varma. 1145-1150 [doi]
• HuaAMS at SemEval-2022 Task 8: Combining Translation and Domain Pre-training for Cross-lingual News Article SimilaritySai Sandeep Sharma Chittilla, Talaat Khalil. 1151-1156 [doi]
• DartmouthCS at SemEval-2022 Task 8: Predicting Multilingual News Article Similarity with Meta-Information and TranslationJoseph Hajjar, Weicheng Ma, Soroush Vosoughi. 1157-1162 [doi]
• Team Innovators at SemEval-2022 for Task 8: Multi-Task Training with Hyperpartisan and Semantic Relation for Multi-Lingual News Article SimilarityNidhir Bhavsar, Rishikesh Devanathan, Aakash Bhatnagar, Muskaan Singh, Petr Motlícek, Tirthankar Ghosal. 1163-1170 [doi]
• OversampledML at SemEval-2022 Task 8: When multilingual news similarity met Zero-shot approachesMayank Jobanputra, Lorena Martín Rodríguez. 1171-1177 [doi]
• Team TMA at SemEval-2022 Task 8: Lightweight and Language-Agnostic News Similarity ClassifierNicolas Stefanovitch. 1178-1183 [doi]
• ITNLP2022 at SemEval-2022 Task 8: Pre-trained Model with Data Augmentation and Voting for Multilingual News SimilarityZhongan Chen, Weiwei Chen, Yunlong Sun, Hongqing Xu, Shuzhe Zhou, Bohan Chen, Chengjie Sun, Yuanchao Liu. 1184-1189 [doi]
• LSX_team5 at SemEval-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover's DistanceStefan Heil, Karina Kopp, Albin Zehe, Konstantin Kobs, Andreas Hotho. 1190-1195 [doi]
• Team dina at SemEval-2022 Task 8: Pre-trained Language Models as Baselines for Semantic SimilarityDina Pisarevskaya, Arkaitz Zubiaga. 1196-1201 [doi]
• TCU at SemEval-2022 Task 8: A Stacking Ensemble Transformer Model for Multilingual News Article SimilarityXiang Luo, Yanqing Niu, Boer Zhu. 1202-1207 [doi]
• Nikkei at SemEval-2022 Task 8: Exploring BERT-based Bi-Encoder Approach for Pairwise Multilingual News Article SimilarityShotaro Ishihara, Hono Shirai. 1208-1214 [doi]
• YNU-HPCC at SemEval-2022 Task 8: Transformer-based Ensemble Model for Multilingual News Article SimilarityZihan Nai, Jin Wang, Xuejie Zhang 0002. 1215-1220 [doi]
• BL.Research at SemEval-2022 Task 8: Using various Semantic Information to evaluate document-level Semantic Textual SimilaritySebastien Dufour, Mohamed Mehdi Kandi, Karim Boutamine, Camille Gosset, Mokhtar Boumedyen Billami, Christophe Bortolaso, Youssef Miloudi. 1221-1228 [doi]
• DataScience-Polimi at SemEval-2022 Task 8: Stacking Language Models to Predict News Article SimilarityMarco Di Giovanni, Thomas Tasca, Marco Brambilla 0001. 1229-1234 [doi]
• WueDevils at SemEval-2022 Task 8: Multilingual News Article Similarity via Pair-Wise Sentence Similarity MatricesDirk Wangsadirdja, Felix Heinickel, Simon Trapp, Albin Zehe, Konstantin Kobs, Andreas Hotho. 1235-1243 [doi]
• SemEval-2022 Task 9: R2VQ - Competence-based Multimodal Question AnsweringJingxuan Tu, Eben Holderness, Marco Maru, Simone Conia, Kyeongmin Rim, Kelley Lynch, Richard Brutti, Roberto Navigli, James Pustejovsky. 1244-1255 [doi]
• HIT&QMUL at SemEval-2022 Task 9: Label-Enclosed Generative Question Answering (LEG-QA)Weihe Zhai, Mingqiang Feng, Arkaitz Zubiaga, Bingquan Liu. 1256-1262 [doi]
• Samsung Research Poland (SRPOL) at SemEval-2022 Task 9: Hybrid Question Answering Using Semantic RolesTomasz Dryjanski, Monika Zaleska, Bartek Kuzma, Artur Blazejewski, Zuzanna Bordzicka, Pawel Bujnowski, Klaudia Firlag, Christian Goltz, Maciej Grabowski, Jakub Jonczyk, Grzegorz Klosinski, Bartlomiej Paziewski, Natalia Paszkiewicz, Jaroslaw Piersa, Piotr Andruszkiewicz. 1263-1273 [doi]
• PINGAN_AI at SemEval-2022 Task 9: Recipe knowledge enhanced model applied in Competence-based Multimodal Question AnsweringZhihao Ruan, Xiaolong Hou, Lian-Xin Jiang. 1274-1279 [doi]
• SemEval 2022 Task 10: Structured Sentiment AnalysisJeremy Barnes, Laura Oberländer, Enrica Troiano, Andrey Kutuzov, Jan Buchmann, Rodrigo Agerri, Lilja Øvrelid, Erik Velldal. 1280-1295 [doi]
• AMEX AI Labs at SemEval-2022 Task 10: Contextualized fine-tuning of BERT for Structured Sentiment AnalysisPratyush Sarangi, Shamika Ganesan, Piyush Arora, Salil Joshi 0001. 1296-1304 [doi]
• ISCAS at SemEval-2022 Task 10: An Extraction-Validation Pipeline for Structured Sentiment AnalysisXinyu Lu, Mengjie Ren, Yaojie Lu 0001, Hongyu Lin. 1305-1312 [doi]
• SenPoi at SemEval-2022 Task 10: Point me to your Opinion, SenPoiJan Pfister, Sebastian Wankerl, Andreas Hotho. 1313-1323 [doi]
• SSN_MLRG1 at SemEval-2022 Task 10: Structured Sentiment Analysis using 2-layer BiLSTMKarun Anantharaman, Divyasri K, Jayannthan Pt, Angel Deborah S, Rajalakshmi Sivanaiah, Sakaya Milton Rajendram, T. T. Mirnalinee. 1324-1328 [doi]
• MT-Speech at SemEval-2022 Task 10: Incorporating Data Augmentation and Auxiliary Task with Cross-Lingual Pretrained Language Model for Structured Sentiment AnalysisCong Chen, Jiansong Chen, Cao Liu, Fan Yang, Guanglu Wan, Jinxiong Xia. 1329-1335 [doi]
• ECNU_ICA at SemEval-2022 Task 10: A Simple and Unified Model for Monolingual and Crosslingual Structured Sentiment AnalysisQi Zhang, Jie Zhou 0015, Qin Chen, Qingchun Bai, Jun Xiao, Liang He 0001. 1336-1342 [doi]
• ZHIXIAOBAO at SemEval-2022 Task 10: Apporoaching Structured Sentiment with Graph ParsingYangkun Lin, Chen Liang, Jing Xu, Chong Yang, Yongliang Wang. 1343-1348 [doi]
• Hitachi at SemEval-2022 Task 10: Comparing Graph- and Seq2Seq-based Models Highlights Difficulty in Structured Sentiment AnalysisGaku Morio, Hiroaki Ozaki, Atsuki Yamaguchi, Yasuhiro Sogawa. 1349-1359 [doi]
• UFRGSent at SemEval-2022 Task 10: Structured Sentiment Analysis using a Question Answering ModelLucas Rafael Costella Pessutto, Viviane P. Moreira. 1360-1365 [doi]
• OPI at SemEval-2022 Task 10: Transformer-based Sequence Tagging with Relation Classification for Structured Sentiment AnalysisRafal Poswiata. 1366-1372 [doi]
• ETMS@IITKGP at SemEval-2022 Task 10: Structured Sentiment Analysis Using A Generative ApproachRaghav R, Adarsh Vemali, Rajdeep Mukherjee. 1373-1381 [doi]
• SLPL-Sentiment at SemEval-2022 Task 10: Making Use of Pre-Trained Model's Attention Values in Structured Sentiment AnalysisSadrodin Barikbin. 1382-1388 [doi]
• LyS_ACoruña at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency ParsingIago Alonso-Alonso, David Vilares, Carlos Gómez-Rodríguez. 1389-1400 [doi]
• SPDB Innovation Lab at SemEval-2022 Task 10: A Novel End-to-End Structured Sentiment Analysis Model based on the ERNIE-MYalong Jia, Zhenghui Ou, Yang Yang. 1401-1405 [doi]
• HITSZ-HLT at SemEval-2022 Task 10: A Span-Relation Extraction Framework for Structured Sentiment AnalysisYihui Li, Yifan Yang, Yice Zhang, Ruifeng Xu. 1406-1411 [doi]
• SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER)Shervin Malmasi, Anjie Fang, Besnik Fetahu, Sudipta Kar, Oleg Rokhlenko. 1412-1437 [doi]
• LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity RecognitionNgoc Lai. 1438-1443 [doi]
• PA Ph&Tech at SemEval-2022 Task 11: NER Task with Ensemble Embedding from Reinforcement LearningQizhi Lin, Changyu Hou, Xiaopeng Wang, Jun Wang, Yixuan Qiao, Peng Jiang, Xiandi Jiang, Benqi Wang, Qifeng Xiao. 1444-1447 [doi]
• UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Transformer-based Models for Complex Named Entity RecognitionElisa Terumi Rubel Schneider, Renzo M. Rivera Zavala, Paloma Martínez, Claudia Moro 0001, Emerson Cabrera Paraiso. 1448-1456 [doi]
• DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity RecognitionXinyu Wang 0013, Yongliang Shen 0001, Jiong Cai, Tao Wang, XiaoBin Wang, Pengjun Xie, Fei Huang 0004, Weiming Lu 0001, Yueting Zhuang, Kewei Tu, Wei Lu 0011, Yong Jiang. 1457-1468 [doi]
• Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource LanguagesAmit Pandey, Swayatta Daw, Narendra Babu Unnam, Vikram Pudi. 1469-1476 [doi]
• AaltoNLP at SemEval-2022 Task 11: Ensembling Task-adaptive Pretrained Transformers for Multilingual Complex NERAapo Pietiläinen, Shaoxiong Ji. 1477-1482 [doi]
• DANGNT-SGU at SemEval-2022 Task 11: Using Pre-trained Language Model for Complex Named Entity RecognitionDang Nguyen, Huy Khac Nguyen Huynh. 1483-1487 [doi]
• OPDAI at SemEval-2022 Task 11: A hybrid approach for Chinese NER using outside Wikipedia knowledgeZe Chen, Kangxu Wang, Jiewen Zheng, Zijian Cai, Jiarong He, Jin Gao. 1488-1493 [doi]
• Sliced at SemEval-2022 Task 11: Bigger, Better? Massively Multilingual LMs for Multilingual Complex NER on an Academic GPU BudgetBarbara Plank. 1494-1500 [doi]
• Infrrd.ai at SemEval-2022 Task 11: A system for named entity recognition using data augmentation, transformer-based sequence labeling model, and EnsembleCRFJianglong He, Akshay Uppal, Mamatha N, Shiv Vignesh, Deepak Kumar, Aditya Kumar Sarda. 1501-1510 [doi]
• UM6P-CS at SemEval-2022 Task 11: Enhancing Multilingual and Code-Mixed Complex Named Entity Recognition via Pseudo Labels using Multilingual TransformerAbdellah El Mekki, Abdelkader El Mahdaouy, Mohammed Akallouch, Ismail Berrada, Ahmed Khoumsi. 1511-1517 [doi]
• CASIA at SemEval-2022 Task 11: Chinese Named Entity Recognition for Complex and Ambiguous EntitiesJia Fu, Zhen Gan, Zhucong Li, Sirui Li, Dianbo Sui, Yubo Chen 0001, Kang Liu 0001, Jun Zhao 0001. 1518-1523 [doi]
• TEAM-Atreides at SemEval-2022 Task 11: On leveraging data augmentation and ensemble to recognize complex Named Entities in BanglaNazia Tasnim, Md. Istiak Hossain Shihab, Asif Shahriyar Sushmit, Steven Bethard, Farig Sadeque. 1524-1530 [doi]
• KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity RecognitionCaleb Martin, Huichen Yang, William H. Hsu. 1531-1535 [doi]
• silpa_nlp at SemEval-2022 Tasks 11: Transformer based NER models for Hindi and Bangla languagesSumit Singh, Pawankumar Jawale, Uma Tiwary. 1536-1542 [doi]
• DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language ModelsHossein Rouhizadeh, Douglas Teodoro. 1543-1548 [doi]
• CSECU-DSG at SemEval-2022 Task 11: Identifying the Multilingual Complex Named Entity in Text Using Stacked Embeddings and Transformer based ApproachAbdul Aziz, Md. Akram Hossain, Abu Nowshed Chy. 1549-1555 [doi]
• CMNEROne at SemEval-2022 Task 11: Code-Mixed Named Entity Recognition by leveraging multilingual dataSuman Dowlagar, Radhika Mamidi. 1556-1561 [doi]
• RACAI at SemEval-2022 Task 11: Complex named entity recognition using a lateral inhibition mechanismVasile Pais. 1562-1569 [doi]
• NamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity RecognitionAmina Miftahova, Alexander Pugachev, Artem Skiba, Katya Artemova, Tatiana Batura, Pavel Braslavski, Vladimir Ivanov 0001. 1570-1575 [doi]
• Raccoons at SemEval-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity RecognitionAtharvan Dogra, Prabsimran Kaur, Guneet Kohli, Jatin Bedi. 1576-1582 [doi]
• SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition TaskFadi Hassan, Wondimagegnhue Tufa, Guillem Collell, Piek Vossen, Lisa Beinborn, Adrian Flanagan, Kuan Eeik Tan. 1583-1592 [doi]
• SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language ModelsChangyu Hou, Jun Wang, Yixuan Qiao, Peng Jiang, Peng Gao, Guotong Xie, Qizhi Lin, Xiaopeng Wang, Xiandi Jiang, Benqi Wang, Qifeng Xiao. 1593-1596 [doi]
• NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF ModelLung-Hao Lee, Chien-Huan Lu, Tzu-Mi Lin. 1597-1602 [doi]
• CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span ClassificationKeyu Pu, Hongyi Liu, Yixiao Yang, Jiangzhou Ji, Wenyi Lv, Yaohan He. 1603-1607 [doi]
• UA-KO at SemEval-2022 Task 11: Data Augmentation and Ensembles for Korean Named Entity RecognitionHyunju Song, Steven Bethard. 1608-1612 [doi]
• USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity RecognitionBeiduo Chen, Jun-Yu Ma, Jiajun Qi, Wu Guo, Zhen-Hua Ling, Quan Liu. 1613-1622 [doi]
• Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NERAmit Pandey, Swayatta Daw, Vikram Pudi. 1623-1629 [doi]
• L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity RecognitionEmanuela Boros, Carlos-Emiliano González-Gallardo, Jose Moreno, Antoine Doucet. 1630-1638 [doi]
• MarSan at SemEval-2022 Task 11: Multilingual complex named entity recognition using T5 and transformer encoderEhsan Tavan, Maryam Najafi. 1639-1647 [doi]
• SU-NLP at SemEval-2022 Task 11: Complex Named Entity Recognition with Entity LinkingBuse Çarik, Fatih Beyhan, Reyyan Yeniterzi. 1648-1653 [doi]
• Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER TaskWeichao Gan, Yuanping Lin, Guangbo Yu, Guimin Chen, Qian Ye. 1654-1664 [doi]
• PAI at SemEval-2022 Task 11: Name Entity Recognition with Contextualized Entity Representations and Robust Loss FunctionsLong Ma, Xiaorong Jian, Xuan Li. 1665-1670 [doi]
• SemEval 2022 Task 12: Symlink - Linking Mathematical Symbols to their DescriptionsViet Dac Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen. 1671-1678 [doi]
• JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their DescriptionsSung-Min Lee, Seung-Hoon Na. 1679-1686 [doi]
• AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify EntitiesNicholas Popovic, Walter Laurito, Michael Färber 0001. 1687-1694 [doi]
• MaChAmp at SemEval-2022 Tasks 2, 3, 4, 6, 10, 11, and 12: Multi-task Multi-lingual Learning for a Pre-selected Set of Semantic DatasetsRob Goot. 1695-1703 [doi]