default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID16th SemEval@NAACL 2022: Seattle, WA, USA
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan:
Proceedings of the 16th International Workshop on Semantic Evaluation, SemEval@NAACL 2022, Seattle, Washington, United States, July 14-15, 2022. Association for Computational Linguistics 2022, ISBN 978-1-955917-80-3 - Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022). - Timothee Mickus
, Kees van Deemter, Mathieu Constant, Denis Paperno:
Semeval-2022 Task 1: CODWOE - Comparing Dictionaries and Word Embeddings. 1-14 - Zhiyong Wang, Ge Zhang, Nineli Lashkarashvili:
1Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse Dictionary Task. 15-22 - Cunliang Kong, Yujie Wang, Ruining Chong, Liner Yang, Hengyuan Zhang, Erhong Yang, Yaping Huang:
BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling. 23-28 - Bin Li, Yixuan Weng, Fei Xia, Shizhu He, Bin Sun, Shutao Li:
LingJing at SemEval-2022 Task 1: Multi-task Self-supervised Pre-training for Multilingual Reverse Dictionary. 29-35 - Damir Korencic, Ivan Grubisic:
IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations. 36-59 - Aditya Srivastava, Harsha Vardhan Vemulapati:
TLDR at SemEval-2022 Task 1: Using Transformers to Learn Dictionaries and Representations. 60-67 - Alfonso Ardoiz, Miguel Ortega-Martín, Óscar García-Sierra, Jorge Álvarez, Ignacio Arranz, Adrián Alonso:
MMG at SemEval-2022 Task 1: A Reverse Dictionary approach based on a review of the dataset from a lexicographic perspective. 68-74 - Pinzhen Chen, Zheng Zhao:
Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions. 75-81 - Eduards Mukans, Gus Strazds, Guntis Barzdins:
RIGA at SemEval-2022 Task 1: Scaling Recurrent Neural Networks for CODWOE Dictionary Modeling. 82-87 - Rafal Cerniavski, Sara Stymne:
Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary? 88-93 - Nihed Bendahman, Julien Breton, Lina Nicolaieff, Mokhtar Boumedyen Billami, Christophe Bortolaso, Youssef Miloudi:
BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencoders. 94-100 - Tran Thi Hong Hanh, Matej Martinc, Matthew Purver, Senja Pollak:
JSI at SemEval-2022 Task 1: CODWOE - Reverse Dictionary: Monolingual and cross-lingual approaches. 101-106 - Harish Tayyar Madabushi, Edward Gow-Smith, Marcos García, Carolina Scarton, Marco Idiart, Aline Villavicencio:
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding. 107-121 - Sami Itkonen, Jörg Tiedemann, Mathias Creutz:
Helsinki-NLP at SemEval-2022 Task 2: A Feature-Based Approach to Multilingual Idiomaticity Detection. 122-134 - Atsuki Yamaguchi, Gaku Morio, Hiroaki Ozaki, Yasuhiro Sogawa:
Hitachi at SemEval-2022 Task 2: On the Effectiveness of Span-based Classification Approaches for Multilingual Idiomaticity Detection. 135-144 - Bradley Hauer, Seeratpal Jaura, Talgat Omarov, Grzegorz Kondrak:
UAlberta at SemEval 2022 Task 2: Leveraging Glosses and Translations for Multilingual Idiomaticity Detection. 145-150 - Youngju Joung, Taeuk Kim:
HYU at SemEval-2022 Task 2: Effective Idiomaticity Detection with Consideration at Different Levels of Contextualization. 151-157 - Dylan Phelps:
drsphelps at SemEval-2022 Task 2: Learning idiom representations using BERTRAM. 158-164 - Yash Jakhotiya, Vaibhav Kumar, Ashwin Pathak, Raj Shah:
JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot Learning. 165-168 - Joanne Boisson, José Camacho-Collados, Luis Espinosa Anke:
CardiffNLP-Metaphor at SemEval-2022 Task 2: Targeted Fine-tuning of Transformer-based Language Models for Idiomaticity Detection. 169-177 - Min Sik Oh:
kpfriends at SemEval-2022 Task 2: NEAMER - Named Entity Augmented Multi-word Expression Recognizer. 178-185 - Daming Lu:
daminglu123 at SemEval-2022 Task 2: Using BERT and LSTM to Do Text Classification. 186-189 - Minghuan Tan:
HiJoNLP at SemEval-2022 Task 2: Detecting Idiomaticity of Multiword Expressions using Multilingual Pretrained Language Models. 190-196 - Xuange Cui, Wei Xiong, Songlin Wang:
ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations. 197-203 - Simone Tedeschi, Roberto Navigli:
NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity Detection. 204-210 - Kuanghong Liu, Jin Wang, Xuejie Zhang:
YNU-HPCC at SemEval-2022 Task 2: Representing Multilingual Idiomaticity based on Contrastive Learning. 211-216 - Lis Pereira, Ichiro Kobayashi:
OCHADAI at SemEval-2022 Task 2: Adversarial Training for Multilingual Idiomaticity Detection. 217-220 - Zheng Chu, Ziqing Yang, Yiming Cui, Zhigang Chen, Ming Liu:
HIT at SemEval-2022 Task 2: Pre-trained Language Model for Idioms Detection. 221-227 - Roberto Zamparelli, Shammur A. Chowdhury, Dominique Brunato, Cristiano Chesi, Felice Dell'Orletta, Md. Arid Hasan, Giulia Venturi:
SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge. 228-238 - Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Bin Sun, Shutao Li, Kang Liu, Jun Zhao:
LingJing at SemEval-2022 Task 3: Applying DeBERTa to Lexical-level Presupposed Relation Taxonomy with Knowledge Transfer. 239-246 - Wessel Poelman, Gijs Danoe, Esther Ploeger, Frank van den Berg, Tommaso Caselli, Lukas Edman:
RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations. 247-254 - Abdul Aziz, Md. Akram Hossain, Abu Nowshed Chy:
CSECU-DSG at SemEval-2022 Task 3: Investigating the Taxonomic Relationship Between Two Arguments using Fusion of Multilingual Transformer Models. 255-259 - Thanet Markchom, Huizhi Liang, Jiaoyan Chen:
UoR-NCL at SemEval-2022 Task 3: Fine-Tuning the BERT-Based Models for Validating Taxonomic Relations. 260-265 - Yue Zhou, Bowei Wei, Jianyu Liu, Yang Yang:
SPDB Innovation Lab at SemEval-2022 Task 3: Recognize Appropriate Taxonomic Relations Between Two Nominal Arguments with ERNIE-M Model. 266-270 - Injy Sarhan, Pablo Mosteiro, Marco Spruit:
UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation. 271-281 - Karl Vetter, Miriam Segiet, Klara Lennermann:
KaMiKla at SemEval-2022 Task 3: AlBERTo, BERT, and CamemBERT - Be(r)tween Taxonomy Detection and Prediction. 282-290 - Yinglu Li, Min Zhang, Xiaosong Qiao, Minghan Wang:
HW-TSC at SemEval-2022 Task 3: A Unified Approach Fine-tuned on Multilingual Pretrained Model for PreTENS. 291-297 - Carla Pérez-Almendros, Luis Espinosa Anke, Steven Schockaert:
SemEval-2022 Task 4: Patronizing and Condescending Language Detection. 298-307 - Mohammad Makahleh, Naba Bani Yaseen, Malak Abdullah:
JUST-DEEP at SemEval-2022 Task 4: Using Deep Learning Techniques to Reveal Patronizing and Condescending Language. 308-312 - Ye Wang, Yanmeng Wang, Baishun Ling, Zexiang Liao, Shaojun Wang, Jing Xiao:
PINGAN Omini-Sinitic at SemEval-2022 Task 4: Multi-prompt Training for Patronizing and Condescending Language Detection. 313-318 - Yong Deng, Chenxiao Dou, Liangyu Chen, Deqiang Miao, Xianghui Sun, Baochang Ma, Xiangang Li:
BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection. 319-323 - Alan Ramponi, Elisa Leonardelli:
DH-FBK at SemEval-2022 Task 4: Leveraging Annotators' Disagreement and Multiple Data Views for Patronizing Language Detection. 324-334 - Dou Hu, Mengyuan Zhou, Xiyang Du, Mengfei Yuan, Jin Zhi, Lian-Xin Jiang, Yang Mo, Xiaofeng Shi:
PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection. 335-343 - Ailneni Rakshitha Rao:
ASRtrans at SemEval-2022 Task 4: Ensemble of Tuned Transformer-based Models for PCL Detection. 344-351 - Samyak Agrawal, Radhika Mamidi:
LastResort at SemEval-2022 Task 4: Towards Patronizing and Condescending Language Detection using Pre-trained Transformer Based Models Ensembles. 352-356 - Felix Herrmann, Julia Krebs:
Felix&Julia at SemEval-2022 Task 4: Patronizing and Condescending Language Detection. 357-362 - Selina Meyer, Maximilian Schmidhuber, Udo Kruschwitz:
MS@IW at SemEval-2022 Task 4: Patronising and Condescending Language Detection with Synthetically Generated Data. 363-368 - Abhishek Singh:
Team LEGO at SemEval-2022 Task 4: Machine Learning Methods for PCL Detection. 369-373 - Rylan Yang, Ethan Chi, Nathan Chi:
RNRE-NLP at SemEval-2022 Task 4: Patronizing and Condescending Language Detection. 374-378 - Xingmeng Zhao, Anthony Rios:
UTSA NLP at SemEval-2022 Task 4: An Exploration of Simple Ensembles of Transformers, Convolutional, and Recurrent Neural Networks. 379-386 - Ali Edalat, Yadollah Yaghoobzadeh, Behnam Bahrak:
AliEdalat at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Fine-tuned Language Models, BERT+BiGRU, and Ensemble Models. 387-393 - Sahil Manoj Bhatt, Manish Shrivastava:
Tesla at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Transformer-based Models with Data Augmentation. 394-399 - Kalaivani Adaikkan, Thenmozhi Durairaj:
SSN_NLP_MLRG at SemEval-2022 Task 4: Ensemble Learning strategies to detect Patronizing and Condescending Language. 400-404 - Sihui Li, Xiaobing Zhou:
Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks. 405-408 - Marco Siino, Marco La Cascia, Ilenia Tinnirello:
McRock at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Multi-Channel CNN, Hybrid LSTM, DistilBERT and XLNet. 409-417 - Upamanyu Dass-Vattam, Spencer Wallace, Rohan Sikand, Zach Witzel, Jillian Tang:
Team Stanford ACMLab at SemEval 2022 Task 4: Textual Analysis of PCL Using Contextual Word Embeddings. 418-420 - Kushagri Tandon, Niladri Chatterjee:
Team LRL_NC at SemEval-2022 Task 4: Binary and Multi-label Classification of PCL using Fine-tuned Transformer-based Models. 421-431 - Junyu Lu, Hao Zhang, Tongyue Zhang, Hongbo Wang, Haohao Zhu, Bo Xu, Hongfei Lin:
GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection. 432-437 - Zihang Liu, Yancheng He, Feiqing Zhuang, Bing Xu:
HITMI&T at SemEval-2022 Task 4: Investigating Task-Adaptive Pretraining And Attention Mechanism On PCL Detection. 438-444 - David Koleczek, Alexander Scarlatos, Preshma Linet Pereira, Siddha Makarand Karkare:
UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language. 445-453 - Wenqiang Bai, Jin Wang, Xuejie Zhang:
YNU-HPCC at SemEval-2022 Task 4: Finetuning Pretrained Language Models for Patronizing and Condescending Language Detection. 454-458 - Laura Vázquez Ramos, Adrián Moreno Monterde, Victoria Pachón, Jacinto Mata:
I2C at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Deep Learning Techniques. 459-463 - Manan Suri:
PiCkLe at SemEval-2022 Task 4: Boosting Pre-trained Language Models with Task Specific Metadata and Cost Sensitive Learning. 464-472 - Tosin P. Adewumi, Lama Alkhaled, Hamam Mokayed, Foteini Liwicki, Marcus Liwicki:
ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language. 473-478 - Jinghua Xu:
Xu at SemEval-2022 Task 4: Pre-BERT Neural Network Methods vs Post-BERT RoBERTa Approach for Patronizing and Condescending Language Detection. 479-484 - Alejandro Mosquera:
Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection. 485-489 - Yves Bestgen:
SATLab at SemEval-2022 Task 4: Trying to Detect Patronizing and Condescending Language with only Character and Word N-grams. 490-495 - Jayant Chhillar:
Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language. 496-502 - Daniel Saeedi, Sirwe Saeedi, Aliakbar Panahi, Alvis Cheuk M. Fong:
CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3. 503-508 - Tudor Dumitrascu, Raluca-Andreea Gînga, Bogdan Dobre, Bogdan Radu Silviu Sielecki:
University of Bucharest Team at Semeval-2022 Task4: Detection and Classification of Patronizing and Condescending Language. 509-514 - Bichu George, S. Adarsh, Nishitkumar Prajapati, Premjith B, Soman Kp:
Amrita_CEN at SemEval-2022 Task 4: Oversampling-based Machine Learning Approach for Detecting Patronizing and Condescending Language. 515-518 - Yaakov HaCohen-Kerner, Ilan Meyrowitsch, Matan Fchima:
JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Preprocessing Methods and various Machine Leaerning Methods. 519-524 - Matej Klemen, Marko Robnik-Sikonja:
ULFRI at SemEval-2022 Task 4: Leveraging uncertainty and additional knowledge for patronizing and condescending language detection. 525-532 - Elisabetta Fersini, Francesca Gasparini, Giulia Rizzi, Aurora Saibene, Berta Chulvi, Paolo Rosso, Alyssa Lees, Jeffrey Sorensen:
SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification. 533-549 - Shankar Mahadevan, Sean Benhur, Roshan Nayak, Malliga Subramanian, Kogilavani Shanmugavadivel, Kanchana Sivanraju, Bharathi Raja Chakravarthi:
Transformers at SemEval-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme Detection. 550-554 - Jin Zhi, Mengyuan Zhou, Mengfei Yuan, Dou Hu, Xiyang Du, Lian-Xin Jiang, Yang Mo, Xiaofeng Shi:
PAIC at SemEval-2022 Task 5: Multi-Modal Misogynous Detection in MEMES with Multi-Task Learning And Multi-model Fusion. 555-562 - Ziming Zhou, Han Zhao, Jingjing Dong, Ning Ding, Xiaolong Liu, Kangli Zhang:
DD-TIG at SemEval-2022 Task 5: Investigating the Relationships Between Multimodal and Unimodal Information in Misogynous Memes Detection and Classification. 563-570 - Rajalakshmi Sivanaiah, Angel Deborah S, Sakaya Milton Rajendram, T. T. Mirnalinee:
TechSSN at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification using Deep Learning Models. 571-574 - Samyak Agrawal, Radhika Mamidi:
LastResort at SemEval-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific Pretraining. 575-580 - Aymé Arango, Jesus Perez-Martin, Arniel Labrada:
HateU at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification. 581-584 - Jing Zhang, Yujin Wang:
SRCB at SemEval-2022 Task 5: Pretraining Based Image to Text Late Sequential Fusion System for Multimodal Misogynous Meme Identification. 585-596 - Ailneni Rakshitha Rao, Arjun Rao:
ASRtrans at SemEval-2022 Task 5: Transformer-based Models for Meme Classification. 597-604 - Edgar Roman-Rangel, Jorge Fuentes-Pacheco, Jorge Hermosillo Valadez:
UAEM-ITAM at SemEval-2022 Task 5: Vision-Language Approach to Recognize Misogynous Content in Memes. 605-609 - Jason Ravagli, Lorenzo Vaiani:
JRLV at SemEval-2022 Task 5: The Importance of Visual Elements for Misogyny Identification in Memes. 610-617 - Andrei Paraschiv, Mihai Dascalu, Dumitru-Clementin Cercel:
UPB at SemEval-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification. 618-625 - Wentao Yu, Benedikt T. Boenninghoff, Jonas Roehrig, Dorothea Kolossa:
RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs. 626-635 - Lei Chen, Hou Wei Chou:
RIT Boston at SemEval-2022 Task 5: Multimedia Misogyny Detection By Using Coherent Visual and Language Features from CLIP Model and Data-centric AI Principle. 636-641 - Paridhi Maheshwari, Sharmila Reddy Nangi:
TeamOtter at SemEval-2022 Task 5: Detecting Misogynistic Content in Multimodal Memes. 642-647 - Chen Tao, Jung-Jae Kim:
taochen at SemEval-2022 Task 5: Multimodal Multitask Learning and Ensemble Learning. 648-653 - Giuseppe Attanasio, Debora Nozza, Federico Bianchi:
MilaNLP at SemEval-2022 Task 5: Using Perceiver IO for Detecting Misogynous Memes with Text and Image Modalities. 654-662 - Arianna Muti, Katerina Korre, Alberto Barrón-Cedeño:
UniBO at SemEval-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in Memes. 663-672 - Tathagata Raha, Sagar Joshi, Vasudeva Varma:
IIITH at SemEval-2022 Task 5: A comparative study of deep learning models for identifying misogynous memes. 673-678 - Ahmed Mahran, Carlo Alessandro Borella, Konstantinos Perifanos:
Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework. 679-688 - Pablo Cordon, Pablo Gonzalez Diaz, Jacinto Mata, Victoria Pachón:
I2C at SemEval-2022 Task 5: Identification of misogyny in internet memes. 689-694 - Gustavo Acauan Lorentz, Viviane P. Moreira:
INF-UFRGS at SemEval-2022 Task 5: analyzing the performance of multimodal models. 695-699 - Yimeng Gu, Ignacio Castro, Gareth Tyson:
MMVAE at SemEval-2022 Task 5: A Multi-modal Multi-task VAE on Misogynous Meme Detection. 700-710 - Da Li, Ming Yi, Yukai He:
AMS_ADRN at SemEval-2022 Task 5: A Suitable Image-text Multimodal Joint Modeling Method for Multi-task Misogyny Identification. 711-717 - Milan Kalkenings, Thomas Mandl:
University of Hildesheim at SemEval-2022 task 5: Combining Deep Text and Image Models for Multimedia Misogyny Detection. 718-723 - Mitra Behzadi, Ali Derakhshan, Ian G. Harris:
Mitra Behzadi at SemEval-2022 Task 5 : Multimedia Automatic Misogyny Identification method based on CLIP. 724-727 - Gagan Sharma, Gajanan Sunil Gitte, Shlok Goyal, Raksha Sharma:
IITR CodeBusters at SemEval-2022 Task 5: Misogyny Identification using Transformers. 728-732 - Shubham Barnwal, Ritesh Kumar, Rajendra Pamula:
IIT DHANBAD CODECHAMPS at SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. 733-735 - Qin Gu, Nino Meisinger, Anna-Katharina Dick:
QiNiAn at SemEval-2022 Task 5: Multi-Modal Misogyny Detection and Classification. 736-741 - José Antonio García-Díaz, Camilo Caparrós-Laiz, Rafael Valencia-García:
UMUTeam at SemEval-2022 Task 5: Combining image and textual embeddings for multi-modal automatic misogyny identification. 742-747 - Chao Han, Jin Wang, Xuejie Zhang:
YNU-HPCC at SemEval-2022 Task 5: Multi-Modal and Multi-label Emotion Classification Based on LXMERT. 748-755 - Sherzod Hakimov, Gullal Singh Cheema, Ralph Ewerth:
TIB-VA at SemEval-2022 Task 5: A Multimodal Architecture for the Detection and Classification of Misogynous Memes. 756-760 - Mayukh Sharma, Ilanthenral Kandasamy, W. B. Vasantha:
R2D2 at SemEval-2022 Task 5: Attention is only as good as its Values! A multimodal system for identifying misogynist memes. 761-770 - Álvaro Huertas-García, Helena Liz, Guillermo Villar-Rodríguez, Alejandro Martín, Javier Huertas-Tato, David Camacho:
AIDA-UPM at SemEval-2022 Task 5: Exploring Multimodal Late Information Fusion for Multimedia Automatic Misogyny Identification. 771-779 - Mohammad Habash, Yahya Daqour, Malak Abdullah, Mahmoud Al-Ayyoub:
YMAI at SemEval-2022 Task 5: Detecting Misogyny in Memes using VisualBERT and MMBT MultiModal Pre-trained Models. 780-784 - Charic Farinango Cuervo, Natalie Parde:
Exploring Contrastive Learning for Multimodal Detection of Misogynistic Memes. 785-792 - Harshvardhan Srivastava:
Poirot at SemEval-2022 Task 5: Leveraging Graph Network for Misogynistic Meme Detection. 793-801 - Ibrahim Abu Farha, Silviu Vlad Oprea, Steven R. Wilson, Walid Magdy:
SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic. 802-814 - Xiyang Du, Dou Hu, Jin Zhi, Lian-Xin Jiang, Xiaofeng Shi:
PALI-NLP at SemEval-2022 Task 6: iSarcasmEval- Fine-tuning the Pre-trained Model for Detecting Intended Sarcasm. 815-819 - Mengfei Yuan, Mengyuan Zhou, Lian-Xin Jiang, Yang Mo, Xiaofeng Shi:
stce at SemEval-2022 Task 6: Sarcasm Detection in English Tweets. 820-826 - Diksha Krishnan, Jerin Mahibha C, Thenmozhi Durairaj:
GetSmartMSEC at SemEval-2022 Task 6: Sarcasm Detection using Contextual Word Embedding with Gaussian model for Irony Type Identification. 827-833 - Aparna K. Ajayan, Krishna Mohanan, Anugraha S, Premjith B, Soman Kp:
Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling. 834-839 - Hamza Alami, Abdessamad Benlahbib, Ahmed Alami:
High Tech team at SemEval-2022 Task 6: Intended Sarcasm Detection for Arabic texts. 840-843 - Abdelkader El Mahdaouy, Abdellah El Mekki, Kabil Essefar, Abderrahman Skiredj, Ismail Berrada:
CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic. 844-850 - Ramdhanush V, Rajalakshmi Sivanaiah, Angel Deborah S, Sakaya Milton Rajendram, T. T. Mirnalinee:
TechSSN at SemEval-2022 Task 6: Intended Sarcasm Detection using Transformer Models. 851-855 - Adrián Moreno Monterde, Laura Vázquez Ramos, Jacinto Mata, Victoria Pachón Álvarez:
I2C at SemEval-2022 Task 6: Intended Sarcasm in English using Deep Learning Techniques. 856-861 - Mostafa Rahgouy, Hamed Babaei Giglou, Taher Rahgooy, Cheryl D. Seals:
NULL at SemEval-2022 Task 6: Intended Sarcasm Detection Using Stylistically Fused Contextualized Representation and Deep Learning. 862-870 - Emmanuel Osei-Brefo, Huizhi Liang:
UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detection. 871-876 - Pablo Gonzalez Diaz, Pablo Cordon, Jacinto Mata, Victoria Pachón:
I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning. 877-880 - Nsrin Ashraf, Fathy Elkazzaz, Mohamed Taha, Hamada A. Nayel, Tarek Elshishtawy:
BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts. 881-884 - Abdulrahman Mohamed Kamr, Ensaf Hussein Mohamed:
akaBERT at SemEval-2022 Task 6: An Ensemble Transformer-based Model for Arabic Sarcasm Detection. 885-890 - Aya Lotfy, Marwan Torki, Nagwa M. El-Makky:
AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning Techniques. 891-895 - Reem Abdel-Salam:
reamtchka at SemEval-2022 Task 6: Investigating the effect of different loss functions for Sarcasm detection for unbalanced datasets. 896-906 - Nikhil Singh:
niksss at SemEval-2022 Task 6: Are Traditionally Pre-Trained Contextual Embeddings Enough for Detecting Intended Sarcasm ? 907-911 - Rishik Lad, Weicheng Ma, Soroush Vosoughi:
Dartmouth at SemEval-2022 Task 6: Detection of Sarcasm. 912-918 - Samantha Huang, Ethan Chi, Nathan Chi:
ISD at SemEval-2022 Task 6: Sarcasm Detection Using Lightweight Models. 919-922 - Mosab Shaheen, Shubham Kumar Nigam:
Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation. 923-937 - Tanuj Singh Shekhawat, Manoj Kumar, Udaybhan Rathore, Aditya Joshi, Jasabanta Patro:
IISERB Brains at SemEval-2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English. 938-944 - Patrick Hantsch, Nadav Chkroun:
connotation_clashers at SemEval-2022 Task 6: The effect of sentiment analysis on sarcasm detection. 945-950 - Jason Angel, Segun Taofeek Aroyehun, Alexander F. Gelbukh:
TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection. 951-955 - Guangmin Zheng, Jin Wang, Xuejie Zhang:
YNU-HPCC at SemEval-2022 Task 6: Transformer-based Model for Intended Sarcasm Detection in English and Arabic. 956-961 - Amirhossein Abaskohi, Arash Rasouli, Tanin Zeraati, Behnam Bahrak:
UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection using generative-based and mutation-based data augmentation. 962-969 - Tudor Manoleasa, Daniela Gifu, Iustin Sandu:
FII UAIC at SemEval-2022 Task 6: iSarcasmEval - Intended Sarcasm Detection in English and Arabic. 970-977 - Maryam Najafi, Ehsan Tavan:
MarSan at SemEval-2022 Task 6: iSarcasm Detection via T5 and Sequence Learners. 978-986 - Olha Kaminska, Chris Cornelis, Véronique Hoste:
LT3 at SemEval-2022 Task 6: Fuzzy-Rough Nearest Neighbor Classification for Sarcasm Detection. 987-992 - Abdessamad Benlahbib, Hamza Alami, Ahmed Alami:
LISACTeam at SemEval-2022 Task 6: A Transformer based Approach for Intended Sarcasm Detection in English Tweets. 993-998 - Ya Han, Yekun Chai, Shuohuan Wang, Yu Sun, Hongyi Huang, Guanghao Chen, Yitong Xu, Yang Yang:
X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection. 999-1004 - Vandita Grover, Hema Banati:
DUCS at SemEval-2022 Task 6: Exploring Emojis and Sentiments for Sarcasm Detection. 1005-1011 - José Antonio García-Díaz, Camilo Caparrós-Laiz, Rafael Valencia-García:
UMUTeam at SemEval-2022 Task 6: Evaluating Transformers for detecting Sarcasm in English and Arabic. 1012-1017 - Mayukh Sharma, Ilanthenral Kandasamy, W. B. Vasantha:
R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm. 1018-1024 - Malak Abdullah, Dalya Faraj, Safa Swedat, Jumana Khrais, Mahmoud Al-Ayyoub:
SarcasmDet at SemEval-2022 Task 6: Detecting Sarcasm using Pre-trained Transformers in English and Arabic Languages. 1025-1030 - Yaakov HaCohen-Kerner, Matan Fchima, Ilan Meyrowitsch:
JCT at SemEval-2022 Task 6-A: Sarcasm Detection in Tweets Written in English and Arabic using Preprocessing Methods and Word N-grams. 1031-1038 - Michael Roth, Talita Anthonio, Anna Sauer:
SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts. 1039-1049 - Daewook Kang, Sung-Min Lee, Eunhwan Park, Seung-Hoon Na:
JBNU-CCLab at SemEval-2022 Task 7: DeBERTa for Identifying Plausible Clarifications in Instructional Texts. 1050-1055 - Xiaosong Qiao, Yinglu Li, Min Zhang, Minghan Wang, Hao Yang, Shimin Tao, Ying Qin:
HW-TSC at SemEval-2022 Task 7: Ensemble Model Based on Pretrained Models for Identifying Plausible Clarifications. 1056-1061 - Samuel Akrah, Ted Pedersen:
DuluthNLP at SemEval-2022 Task 7: Classifying Plausible Alternatives with Pre-trained ELECTRA. 1062-1066 - Thomas Yim, Junha Lee, Rishi Verma, Scott Hickmann, Annie Zhu, Camron Sallade, Ian Ng, Ryan Chi, Patrick Liu:
Stanford MLab at SemEval 2022 Task 7: Tree- and Transformer-Based Methods for Clarification Plausibility. 1067-1070 - Mohammadmahdi Nouriborji, Omid Rohanian, David A. Clifton:
Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression. 1071-1077 - Junyuan Shang, Shuohuan Wang, Yu Sun, Yanjun Yu, Yue Zhou, Li Xiang, Guixiu Yang:
X-PuDu at SemEval-2022 Task 7: A Replaced Token Detection Task Pre-trained Model with Pattern-aware Ensembling for Identifying Plausible Clarifications. 1078-1083 - Mengyuan Zhou, Dou Hu, Mengfei Yuan, Jin Zhi, Xiyang Du, Lian-Xin Jiang, Yang Mo, Xiaofeng Shi:
PALI at SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts. 1084-1089 - Nikhil Singh:
niksss at SemEval-2022 Task7: Transformers for Grading the Clarifications on Instructional Texts. 1090-1093 - Xi Chen, Ali Zeynali, Chico Q. Camargo, Fabian Flöck, Devin Gaffney, Przemyslaw A. Grabowicz, Scott Hale, David Jurgens, Mattia Samory:
SemEval-2022 Task 8: Multilingual news article similarity. 1094-1106 - Elaine Zosa, Emanuela Boros, Boshko Koloski, Lidia Pivovarova:
EMBEDDIA at SemEval-2022 Task 8: Investigating Sentence, Image, and Knowledge Graph Representations for Multilingual News Article Similarity. 1107-1113 - Zihang Xu, Ziqing Yang, Yiming Cui, Zhigang Chen:
HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity. 1114-1120 - Iknoor Singh, Yue Li, Melissa Thong, Carolina Scarton:
GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity. 1121-1128 - Nikhil Goel, Ranjith Reddy Bommidi:
SemEval-2022 Task 8: Multi-lingual News Article Similarity. 1129-1135 - Mikhail Kuimov, Daryna Dementieva, Alexander Panchenko:
SkoltechNLP at SemEval-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector Representations. 1136-1144 - Sagar Joshi, Dhaval Taunk, Vasudeva Varma:
IIIT-MLNS at SemEval-2022 Task 8: Siamese Architecture for Modeling Multilingual News Similarity. 1145-1150 - Sai Sandeep Sharma Chittilla, Talaat Khalil:
HuaAMS at SemEval-2022 Task 8: Combining Translation and Domain Pre-training for Cross-lingual News Article Similarity. 1151-1156 - Joseph Hajjar, Weicheng Ma, Soroush Vosoughi:
DartmouthCS at SemEval-2022 Task 8: Predicting Multilingual News Article Similarity with Meta-Information and Translation. 1157-1162 - Nidhir Bhavsar, Rishikesh Devanathan, Aakash Bhatnagar, Muskaan Singh, Petr Motlícek, Tirthankar Ghosal:
Team Innovators at SemEval-2022 for Task 8: Multi-Task Training with Hyperpartisan and Semantic Relation for Multi-Lingual News Article Similarity. 1163-1170 - Mayank Jobanputra, Lorena Martín Rodríguez:
OversampledML at SemEval-2022 Task 8: When multilingual news similarity met Zero-shot approaches. 1171-1177 - Nicolas Stefanovitch:
Team TMA at SemEval-2022 Task 8: Lightweight and Language-Agnostic News Similarity Classifier. 1178-1183 - Zhongan Chen, Weiwei Chen, YunLong Sun, Hongqing Xu, Shuzhe Zhou, Bohan Chen, Chengjie Sun, Yuanchao Liu:
ITNLP2022 at SemEval-2022 Task 8: Pre-trained Model with Data Augmentation and Voting for Multilingual News Similarity. 1184-1189 - Stefan Heil, Karina Kopp, Albin Zehe, Konstantin Kobs, Andreas Hotho:
LSX_team5 at SemEval-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover's Distance. 1190-1195 - Dina Pisarevskaya, Arkaitz Zubiaga:
Team dina at SemEval-2022 Task 8: Pre-trained Language Models as Baselines for Semantic Similarity. 1196-1201 - Xiang Luo, Yanqing Niu, Boer Zhu:
TCU at SemEval-2022 Task 8: A Stacking Ensemble Transformer Model for Multilingual News Article Similarity. 1202-1207 - Shotaro Ishihara, Hono Shirai:
Nikkei at SemEval-2022 Task 8: Exploring BERT-based Bi-Encoder Approach for Pairwise Multilingual News Article Similarity. 1208-1214 - Zihan Nai, Jin Wang, Xuejie Zhang:
YNU-HPCC at SemEval-2022 Task 8: Transformer-based Ensemble Model for Multilingual News Article Similarity. 1215-1220 - Sebastien Dufour, Mohamed Mehdi Kandi, Karim Boutamine, Camille Gosset, Mokhtar Boumedyen Billami, Christophe Bortolaso, Youssef Miloudi:
BL.Research at SemEval-2022 Task 8: Using various Semantic Information to evaluate document-level Semantic Textual Similarity. 1221-1228 - Marco Di Giovanni, Thomas Tasca, Marco Brambilla:
DataScience-Polimi at SemEval-2022 Task 8: Stacking Language Models to Predict News Article Similarity. 1229-1234 - Dirk Wangsadirdja, Felix Heinickel, Simon Trapp, Albin Zehe, Konstantin Kobs, Andreas Hotho:
WueDevils at SemEval-2022 Task 8: Multilingual News Article Similarity via Pair-Wise Sentence Similarity Matrices. 1235-1243 - Jingxuan Tu, Eben Holderness, Marco Maru, Simone Conia, Kyeongmin Rim, Kelley Lynch, Richard Brutti, Roberto Navigli, James Pustejovsky:
SemEval-2022 Task 9: R2VQ - Competence-based Multimodal Question Answering. 1244-1255 - Weihe Zhai, Mingqiang Feng, Arkaitz Zubiaga, Bingquan Liu:
HIT&QMUL at SemEval-2022 Task 9: Label-Enclosed Generative Question Answering (LEG-QA). 1256-1262 - Tomasz 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:
Samsung Research Poland (SRPOL) at SemEval-2022 Task 9: Hybrid Question Answering Using Semantic Roles. 1263-1273 - Zhihao Ruan, Xiaolong Hou, Lian-Xin Jiang:
PINGAN_AI at SemEval-2022 Task 9: Recipe knowledge enhanced model applied in Competence-based Multimodal Question Answering. 1274-1279 - Jeremy Barnes, Laura Oberländer, Enrica Troiano, Andrey Kutuzov, Jan Buchmann, Rodrigo Agerri, Lilja Øvrelid, Erik Velldal:
SemEval 2022 Task 10: Structured Sentiment Analysis. 1280-1295 - Pratyush Sarangi, Shamika Ganesan, Piyush Arora, Salil Joshi:
AMEX AI Labs at SemEval-2022 Task 10: Contextualized fine-tuning of BERT for Structured Sentiment Analysis. 1296-1304 - Xinyu Lu, Mengjie Ren, Yaojie Lu, Hongyu Lin:
ISCAS at SemEval-2022 Task 10: An Extraction-Validation Pipeline for Structured Sentiment Analysis. 1305-1312 - Jan Pfister, Sebastian Wankerl, Andreas Hotho:
SenPoi at SemEval-2022 Task 10: Point me to your Opinion, SenPoi. 1313-1323 - Karun Anantharaman, Divyasri K, Jayannthan Pt, Angel Deborah S, Rajalakshmi Sivanaiah, Sakaya Milton Rajendram, T. T. Mirnalinee:
SSN_MLRG1 at SemEval-2022 Task 10: Structured Sentiment Analysis using 2-layer BiLSTM. 1324-1328 - Cong Chen, Jiansong Chen, Cao Liu, Fan Yang, Guanglu Wan, Jinxiong Xia:
MT-Speech at SemEval-2022 Task 10: Incorporating Data Augmentation and Auxiliary Task with Cross-Lingual Pretrained Language Model for Structured Sentiment Analysis. 1329-1335 - Qi Zhang, Jie Zhou, Qin Chen, Qingchun Bai, Jun Xiao, Liang He:
ECNU_ICA at SemEval-2022 Task 10: A Simple and Unified Model for Monolingual and Crosslingual Structured Sentiment Analysis. 1336-1342 - Yangkun Lin, Chen Liang, Jing Xu, Chong Yang, Yongliang Wang:
ZHIXIAOBAO at SemEval-2022 Task 10: Apporoaching Structured Sentiment with Graph Parsing. 1343-1348 - Gaku Morio, Hiroaki Ozaki, Atsuki Yamaguchi, Yasuhiro Sogawa:
Hitachi at SemEval-2022 Task 10: Comparing Graph- and Seq2Seq-based Models Highlights Difficulty in Structured Sentiment Analysis. 1349-1359 - Lucas Rafael Costella Pessutto, Viviane P. Moreira:
UFRGSent at SemEval-2022 Task 10: Structured Sentiment Analysis using a Question Answering Model. 1360-1365 - Rafal Poswiata:
OPI at SemEval-2022 Task 10: Transformer-based Sequence Tagging with Relation Classification for Structured Sentiment Analysis. 1366-1372 - Raghav R, Adarsh Vemali, Rajdeep Mukherjee:
ETMS@IITKGP at SemEval-2022 Task 10: Structured Sentiment Analysis Using A Generative Approach. 1373-1381 - Sadrodin Barikbin:
SLPL-Sentiment at SemEval-2022 Task 10: Making Use of Pre-Trained Model's Attention Values in Structured Sentiment Analysis. 1382-1388 - Iago Alonso-Alonso, David Vilares, Carlos Gómez-Rodríguez:
LyS_ACoruña at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency Parsing. 1389-1400 - Yalong Jia, Zhenghui Ou, Yang Yang:
SPDB Innovation Lab at SemEval-2022 Task 10: A Novel End-to-End Structured Sentiment Analysis Model based on the ERNIE-M. 1401-1405 - Yihui Li, Yifan Yang, Yice Zhang, Ruifeng Xu:
HITSZ-HLT at SemEval-2022 Task 10: A Span-Relation Extraction Framework for Structured Sentiment Analysis. 1406-1411 - Shervin Malmasi, Anjie Fang, Besnik Fetahu, Sudipta Kar, Oleg Rokhlenko:
SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER). 1412-1437 - Ngoc Minh Lai:
LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition. 1438-1443 - Qizhi Lin, Changyu Hou, Xiaopeng Wang, Jun Wang, Yixuan Qiao, Peng Jiang, Xiandi Jiang, Benqi Wang, Qifeng Xiao:
PA Ph&Tech at SemEval-2022 Task 11: NER Task with Ensemble Embedding from Reinforcement Learning. 1444-1447 - Elisa Terumi Rubel Schneider, Renzo M. Rivera Zavala, Paloma Martínez, Claudia Moro, Emerson Cabrera Paraiso:
UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Transformer-based Models for Complex Named Entity Recognition. 1448-1456 - Xinyu Wang, Yongliang Shen, Jiong Cai, Tao Wang, Xiaobin Wang, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang, Kewei Tu, Wei Lu, Yong Jiang:
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition. 1457-1468 - Amit Pandey, Swayatta Daw, Narendra Babu Unnam, Vikram Pudi:
Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages. 1469-1476 - Aapo Pietiläinen, Shaoxiong Ji:
AaltoNLP at SemEval-2022 Task 11: Ensembling Task-adaptive Pretrained Transformers for Multilingual Complex NER. 1477-1482 - Dang Nguyen, Huy Khac Nguyen Huynh:
DANGNT-SGU at SemEval-2022 Task 11: Using Pre-trained Language Model for Complex Named Entity Recognition. 1483-1487 - Ze Chen, Kangxu Wang, Jiewen Zheng, Zijian Cai, Jiarong He, Jin Gao:
OPDAI at SemEval-2022 Task 11: A hybrid approach for Chinese NER using outside Wikipedia knowledge. 1488-1493 - Barbara Plank:
Sliced at SemEval-2022 Task 11: Bigger, Better? Massively Multilingual LMs for Multilingual Complex NER on an Academic GPU Budget. 1494-1500 - Jianglong He, Akshay Uppal, Mamatha N, Shiv Vignesh, Deepak Kumar, Aditya Kumar Sarda:
Infrrd.ai at SemEval-2022 Task 11: A system for named entity recognition using data augmentation, transformer-based sequence labeling model, and EnsembleCRF. 1501-1510 - Abdellah El Mekki, Abdelkader El Mahdaouy, Mohammed Akallouch, Ismail Berrada, Ahmed Khoumsi:
UM6P-CS at SemEval-2022 Task 11: Enhancing Multilingual and Code-Mixed Complex Named Entity Recognition via Pseudo Labels using Multilingual Transformer. 1511-1517 - Jia Fu, Zhen Gan, Zhucong Li, Sirui Li, Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao:
CASIA at SemEval-2022 Task 11: Chinese Named Entity Recognition for Complex and Ambiguous Entities. 1518-1523 - Nazia Tasnim, Md. Istiak Hossain Shihab, Asif Shahriyar Sushmit, Steven Bethard, Farig Sadeque:
TEAM-Atreides at SemEval-2022 Task 11: On leveraging data augmentation and ensemble to recognize complex Named Entities in Bangla. 1524-1530 - Caleb Martin, Huichen Yang, William H. Hsu:
KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition. 1531-1535 - Sumit Singh, Pawankumar Jawale, Uma Shanker Tiwary:
silpa_nlp at SemEval-2022 Tasks 11: Transformer based NER models for Hindi and Bangla languages. 1536-1542 - Hossein Rouhizadeh, Douglas Teodoro:
DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models. 1543-1548 - Abdul Aziz, Md. Akram Hossain, Abu Nowshed Chy:
CSECU-DSG at SemEval-2022 Task 11: Identifying the Multilingual Complex Named Entity in Text Using Stacked Embeddings and Transformer based Approach. 1549-1555 - Suman Dowlagar, Radhika Mamidi:
CMNEROne at SemEval-2022 Task 11: Code-Mixed Named Entity Recognition by leveraging multilingual data. 1556-1561 - Vasile Pais:
RACAI at SemEval-2022 Task 11: Complex named entity recognition using a lateral inhibition mechanism. 1562-1569 - Amina Miftahova, Alexander Pugachev, Artem Skiba, Ekaterina Artemova, Tatiana Batura, Pavel Braslavski, Vladimir Ivanov:
NamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition. 1570-1575 - Atharvan Dogra, Prabsimran Kaur, Guneet Singh Kohli, Jatin Bedi:
Raccoons at SemEval-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity Recognition. 1576-1582 - Fadi Hassan, Wondimagegnhue Tufa, Guillem Collell, Piek Vossen, Lisa Beinborn, Adrian Flanagan, Kuan Eeik Tan:
SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task. 1583-1592 - Changyu Hou, Jun Wang, Yixuan Qiao, Peng Jiang, Peng Gao, Guotong Xie, Qizhi Lin, Xiaopeng Wang, Xiandi Jiang, Benqi Wang, Qifeng Xiao:
SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models. 1593-1596 - Lung-Hao Lee, Chien-Huan Lu, Tzu-Mi Lin:
NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model. 1597-1602 - Keyu Pu, Hongyi Liu, Yixiao Yang, Jiangzhou Ji, Wenyi Lv, Yaohan He:
CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification. 1603-1607 - Hyunju Song, Steven Bethard:
UA-KO at SemEval-2022 Task 11: Data Augmentation and Ensembles for Korean Named Entity Recognition. 1608-1612 - Beiduo Chen, Jun-Yu Ma, Jiajun Qi, Wu Guo, Zhen-Hua Ling, Quan Liu:
USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition. 1613-1622 - Amit Pandey, Swayatta Daw, Vikram Pudi:
Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER. 1623-1629 - Emanuela Boros, Carlos-Emiliano González-Gallardo, José G. Moreno, Antoine Doucet:
L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition. 1630-1638 - Ehsan Tavan, Maryam Najafi:
MarSan at SemEval-2022 Task 11: Multilingual complex named entity recognition using T5 and transformer encoder. 1639-1647 - Buse Çarik, Fatih Beyhan, Reyyan Yeniterzi:
SU-NLP at SemEval-2022 Task 11: Complex Named Entity Recognition with Entity Linking. 1648-1653 - Weichao Gan, Yuanping Lin, Guangbo Yu, Guimin Chen, Qian Ye:
Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task. 1654-1664 - Long Ma, Xiaorong Jian, Xuan Li:
PAI at SemEval-2022 Task 11: Name Entity Recognition with Contextualized Entity Representations and Robust Loss Functions. 1665-1670 - Viet Dac Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen:
SemEval 2022 Task 12: Symlink - Linking Mathematical Symbols to their Descriptions. 1671-1678 - Sung-Min Lee, Seung-Hoon Na:
JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their Descriptions. 1679-1686 - Nicholas Popovic, Walter Laurito, Michael Färber:
AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify Entities. 1687-1694 - Rob van der Goot:
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 Datasets. 1695-1703
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2026-01-15 23:59 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
