Table of Contents

• Frontmatter [doi]
• SemEval-2021 Task 1: Lexical Complexity PredictionMatthew Shardlow, Richard Evans, Gustavo Henrique Paetzold, Marcos Zampieri. 1-16 [doi]
• OCHADAI-KYOTO at SemEval-2021 Task 1: Enhancing Model Generalization and Robustness for Lexical Complexity PredictionYuki Taya, Lis Kanashiro Pereira, Fei Cheng, Ichiro Kobayashi. 17-23 [doi]
• SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC)Federico Martelli, Najla Kalach, Gabriele Tola, Roberto Navigli. 24-36 [doi]
• SemEval-2021 Task 4: Reading Comprehension of Abstract MeaningBoyuan Zheng, Xiaoyu Yang, Yu-Ping Ruan, Zhen-Hua Ling, Quan Liu, Si Wei, Xiaodan Zhu. 37-50 [doi]
• TA-MAMC at SemEval-2021 Task 4: Task-adaptive Pretraining and Multi-head Attention for Abstract Meaning Reading ComprehensionJing Zhang, Yimeng Zhuang, Yinpei Su. 51-58 [doi]
• SemEval-2021 Task 5: Toxic Spans DetectionJohn Pavlopoulos, Jeffrey Sorensen, Leo Laugier, Ion Androutsopoulos. 59-69 [doi]
• SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and ImagesDimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, Giovanni Da San Martino. 70-98 [doi]
• Alpha at SemEval-2021 Task 6: Transformer Based Propaganda ClassificationZhida Feng, Jiji Tang, Jiaxiang Liu 0004, Weichong Yin, Shikun Feng, Yu Sun, Li Chen. 99-104 [doi]
• SemEval 2021 Task 7: HaHackathon, Detecting and Rating Humor and OffenseJ. A Meaney, Steven R. Wilson 0001, Luis Chiruzzo, Adam Lopez, Walid Magdy. 105-119 [doi]
• LangResearchLab NC at SemEval-2021 Task 1: Linguistic Feature Based Modelling for Lexical ComplexityRaksha Agarwal, Niladri Chatterjee. 120-125 [doi]
• Complex words identification using word-level features for SemEval-2020 Task 1Jenny Alexandra Ortiz Zambrano, Arturo Montejo Ráez. 126-129 [doi]
• TUDA-CCL at SemEval-2021 Task 1: Using Gradient-boosted Regression Tree Ensembles Trained on a Heterogeneous Feature Set for Predicting Lexical ComplexitySebastian Gombert, Sabine Bartsch. 130-137 [doi]
• JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity PredictionChaya Liebeskind, Otniel Elkayam, Shmuel Liebeskind. 138-143 [doi]
• IAPUCP at SemEval-2021 Task 1: Stacking Fine-Tuned Transformers is Almost All You Need for Lexical Complexity PredictionKervy Rivas Rojas, Fernando Alva-Manchego. 144-149 [doi]
• Uppsala NLP at SemEval-2021 Task 2: Multilingual Language Models for Fine-tuning and Feature Extraction in Word-in-Context DisambiguationHuiling You, Xingran Zhu, Sara Stymne. 150-156 [doi]
• SkoltechNLP at SemEval-2021 Task 2: Generating Cross-Lingual Training Data for the Word-in-Context TaskAnton Razzhigaev, Nikolay Arefyev, Alexander Panchenko. 157-162 [doi]
• Zhestyatsky at SemEval-2021 Task 2: ReLU over Cosine Similarity for BERT Fine-tuningBoris Zhestiankin, Maria Ponomareva. 163-168 [doi]
• SzegedAI at SemEval-2021 Task 2: Zero-shot Approach for Multilingual and Cross-lingual Word-in-Context DisambiguationGábor Berend. 169-174 [doi]
• ReCAM@IITK at SemEval-2021 Task 4: BERT and ALBERT based Ensemble for Abstract Word PredictionAbhishek Mittal, Ashutosh Modi. 175-182 [doi]
• ECNU_ICA_1 SemEval-2021 Task 4: Leveraging Knowledge-enhanced Graph Attention Networks for Reading Comprehension of Abstract MeaningPingsheng Liu, Linlin Wang, Qian Zhao, Hao Chen, Yuxi Feng, Xin Lin, Liang He 0001. 183-188 [doi]
• LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and VotingAbheesht Sharma, Harshit Pandey, Gunjan Chhablani, Yash Bhartia, Tirtharaj Dash. 189-198 [doi]
• IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back TranslationYuqiang Xie, Luxi Xing, Wei Peng, Yue Hu 0002. 199-204 [doi]
• NLP-IIS@UT at SemEval-2021 Task 4: Machine Reading Comprehension using the Long Document TransformerHossein Basafa, Sajad Movahedi, Ali Ebrahimi, Azadeh Shakery, Heshaam Faili. 205-210 [doi]
• IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans DetectionArchit Bansal, Abhay Kaushik, Ashutosh Modi. 211-219 [doi]
• UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words ModelAkbar Karimi, Leonardo Rossi, Andrea Prati 0001. 220-224 [doi]
• UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans DetectionAndrei Paraschiv, Dumitru-Clementin Cercel, Mihai Dascalu. 225-232 [doi]
• NLRG at SemEval-2021 Task 5: Toxic Spans Detection Leveraging BERT-based Token Classification and Span Prediction TechniquesGunjan Chhablani, Abheesht Sharma, Harshit Pandey, Yash Bhartia, Shan Suthaharan. 233-242 [doi]
• UoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span PredictionErik Yan, Harish Tayyar Madabushi. 243-248 [doi]
• Cisco at SemEval-2021 Task 5: What's Toxic?: Leveraging Transformers for Multiple Toxic Span Extraction from Online CommentsSreyan Ghosh, Sonal Kumar. 249-257 [doi]
• MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans DetectionZhen Wang, Hongjie Fan, Junfei Liu. 258-262 [doi]
• HamiltonDinggg at SemEval-2021 Task 5: Investigating Toxic Span Detection using RoBERTa Pre-trainingHuiyang Ding, David Jurgens. 263-269 [doi]
• WVOQ at SemEval-2021 Task 6: BART for Span Detection and ClassificationCees Roele. 270-274 [doi]
• HumorHunter at SemEval-2021 Task 7: Humor and Offense Recognition with Disentangled AttentionYubo Xie, Junze Li, Pearl Pu. 275-280 [doi]
• Grenzlinie at SemEval-2021 Task 7: Detecting and Rating Humor and OffenseRenyuan Liu, Xiaobing Zhou. 281-285 [doi]
• abcbpc at SemEval-2021 Task 7: ERNIE-based Multi-task Model for Detecting and Rating Humor and OffenseChao Pang, Xiaoran Fan, Weiyue Su, Xuyi Chen, Shuohuan Wang, Jiaxiang Liu 0004, Xuan Ouyang, Shikun Feng, Yu Sun. 286-289 [doi]
• Humor@IITK at SemEval-2021 Task 7: Large Language Models for Quantifying Humor and OffensivenessAishwarya Gupta, Avik Pal, Bholeshwar Khurana, Lakshay Tyagi, Ashutosh Modi. 290-296 [doi]
• RoMa at SemEval-2021 Task 7: A Transformer-based Approach for Detecting and Rating Humor and OffenseRoberto Labadie, Mariano Jason Rodriguez Cisnero, Reynier Ortega Bueno, Paolo Rosso. 297-305 [doi]
• SemEval-2021 Task 8: MeasEval - Extracting Counts and Measurements and their Related ContextsCorey A. Harper, Jessica Cox, Curt Kohler, Antony Scerri, Ron Daniel Jr., Paul Groth. 306-316 [doi]
• SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS)Nancy Xin Ru Wang, Diwakar Mahajan, Marina Danilevsky, Sara Rosenthal. 317-326 [doi]
• BreakingBERT@IITK at SemEval-2021 Task 9: Statement Verification and Evidence Finding with TablesAditya Jindal, Ankur Gupta, Jaya Srivastava, Preeti Menghwani, Vijit Malik, Vishesh Kaushik, Ashutosh Modi. 327-337 [doi]
• SemEval-2021 Task 12: Learning with DisagreementsAlexandra Uma, Tommaso Fornaciari, Anca Dumitrache, Tristan Miller, Jon Chamberlain, Barbara Plank, Edwin Simpson, Massimo Poesio. 338-347 [doi]
• SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic ProcessingEgoitz Laparra, Xin Su, Yiyun Zhao, Özlem Uzuner, Timothy Miller, Steven Bethard. 348-356 [doi]
• BLCUFIGHT at SemEval-2021 Task 10: Novel Unsupervised Frameworks For Source-Free Domain AdaptationWeikang Wang, Yi Wu, Yixiang Liu, Pengyuan Liu. 357-363 [doi]
• SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge GraphJennifer D'Souza, Sören Auer, Ted Pedersen. 364-376 [doi]
• UIUC_BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP ContributionsHaoyang Liu, Maria Janina Sarol, Halil Kilicoglu. 377-386 [doi]
• KGP at SemEval-2021 Task 8: Leveraging Multi-Staged Language Models for Extracting Measurements, their Attributes and RelationsNeel Karia, Ayush Kaushal, Faraaz Mallick. 387-396 [doi]
• DPR at SemEval-2021 Task 8: Dynamic Path Reasoning for Measurement Relation ExtractionAmir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen. 397-403 [doi]
• CLaC-np at SemEval-2021 Task 8: Dependency DGCNNNihatha Lathiff, Pavel Khloponin, Sabine Bergler. 404-409 [doi]
• CLaC-BP at SemEval-2021 Task 8: SciBERT Plus Rules for MeasEvalBenjamin Thérien, Parsa Bagherzadeh, Sabine Bergler. 410-415 [doi]
• THiFly_Queens at SemEval-2021 Task 9: Two-stage Statement Verification with Adaptive Ensembling and Slot-based OperationYuxuan Zhou, Kaiyin Zhou, Xien Liu, Ji Wu, Xiaodan Zhu. 416-422 [doi]
• TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-trainingThomas Müller 0009, Julian Eisenschlos, Syrine Krichene. 423-430 [doi]
• BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular DataAbdullatif Köksal, Yusuf Yüksel, Bekir Yildirim, Arzucan Özgür. 431-437 [doi]
• IITK at SemEval-2021 Task 10: Source-Free Unsupervised Domain Adaptation using Class PrototypesHarshit Kumar, Jinang Shah, Nidhi Hegde, Priyanshu Gupta, Vaibhav Jindal, Ashutosh Modi. 438-444 [doi]
• PTST-UoM at SemEval-2021 Task 10: Parsimonious Transfer for Sequence TaggingKemal Kurniawan, Lea Frermann, Philip Schulz, Trevor Cohn. 445-451 [doi]
• Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain AdaptationSangwon Yoon, Yanghoon Kim, Kyomin Jung. 452-457 [doi]
• The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain AdaptationXin Su, Yiyun Zhao, Steven Bethard. 458-466 [doi]
• KnowGraph@IITK at SemEval-2021 Task 11: Building Knowledge Graph for NLP ResearchShashank Shailabh, Sajal Chaurasia, Ashutosh Modi. 467-477 [doi]
• YNU-HPCC at SemEval-2021 Task 11: Using a BERT Model to Extract Contributions from NLP Scholarly ArticlesXinge Ma, Jin Wang, Xuejie Zhang. 478-484 [doi]
• ITNLP at SemEval-2021 Task 11: Boosting BERT with Sampling and Adversarial Training for Knowledge ExtractionGenyu Zhang, Yu Su, Changhong He, Lei Lin 0001, Chengjie Sun, Lili Shan. 485-489 [doi]
• Duluth at SemEval-2021 Task 11: Applying DeBERTa to Contributing Sentence Selection and Dependency Parsing for Entity ExtractionAnna Martin, Ted Pedersen. 490-501 [doi]
• INNOVATORS at SemEval-2021 Task-11: A Dependency Parsing and BERT-based model for Extracting Contribution Knowledge from Scientific PapersHardik Arora, Tirthankar Ghosal, Sandeep Kumar, Suraj Patwal, Phil Gooch. 502-510 [doi]
• MCL@IITK at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation using Augmented Data, Signals, and TransformersRohan Gupta, Jay Mundra, Deepak Mahajan, Ashutosh Modi. 511-520 [doi]
• HITSZ-HLT at SemEval-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span DetectionQinglin Zhu, Zijie Lin, Yice Zhang, Jingyi Sun, Xiang Li, Qihui Lin, Yixue Dang, Ruifeng Xu. 521-526 [doi]
• SarcasmDet at SemEval-2021 Task 7: Detect Humor and Offensive based on Demographic Factors using RoBERTa Pre-trained ModelDalya Faraj, Malak Abdullah. 527-533 [doi]
• UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements as Multi-Turn Question AnsweringAndrei-Marius Avram, George-Eduard Zaharia, Dumitru-Clementin Cercel, Mihai Dascalu. 534-540 [doi]
• IITK@LCP at SemEval-2021 Task 1: Classification for Lexical Complexity Regression TaskNeil Rajiv Shirude, Sagnik Mukherjee, Tushar Shandhilya, Ananta Mukherjee, Ashutosh Modi. 541-547 [doi]
• LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity PredictionAbhinandan Tejalkumar Desai, Kai North, Marcos Zampieri, Christopher Homan. 548-553 [doi]
• Alejandro Mosquera at SemEval-2021 Task 1: Exploring Sentence and Word Features for Lexical Complexity PredictionAlejandro Mosquera. 554-559 [doi]
• CompNA at SemEval-2021 Task 1: Prediction of lexical complexity analyzing heterogeneous featuresGiuseppe Vettigli, Antonio Sorgente. 560-564 [doi]
• PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP)Rong Xiang, Jinghang Gu, Emmanuele Chersoni, Wenjie Li 0002, Qin Lu 0001, Chu-Ren Huang. 565-570 [doi]
• LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association MeasuresYves Bestgen. 571-577 [doi]
• DeepBlueAI at SemEval-2021 Task 1: Lexical Complexity Prediction with A Deep Ensemble ApproachChunguang Pan, Bingyan Song, Shengguang Wang, Zhipeng Luo. 578-584 [doi]
• CS-UM6P at SemEval-2021 Task 1: A Deep Learning Model-based Pre-trained Transformer Encoder for Lexical ComplexityNabil El Mamoun, Abdelkader El Mahdaouy, Abdellah El Mekki, Kabil Essefar, Ismail Berrada. 585-589 [doi]
• Cambridge at SemEval-2021 Task 1: An Ensemble of Feature-Based and Neural Models for Lexical Complexity PredictionZheng Yuan, Gladys Tyen, David Strohmaier. 590-597 [doi]
• hub at SemEval-2021 Task 1: Fusion of Sentence and Word Frequency to Predict Lexical ComplexityBo Huang, Yang Bai, Xiaobing Zhou. 598-602 [doi]
• Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word IdentificationRobert Flynn, Matthew Shardlow. 603-608 [doi]
• UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity PredictionGeorge-Eduard Zaharia, Dumitru-Clementin Cercel, Mihai Dascalu. 609-616 [doi]
• UTFPR at SemEval-2021 Task 1: Complexity Prediction by Combining BERT Vectors and Classic FeaturesGustavo Henrique Paetzold. 617-622 [doi]
• RG PA at SemEval-2021 Task 1: A Contextual Attention-based Model with RoBERTa for Lexical Complexity PredictionGang Rao, Maochang Li, Xiaolong Hou, Lian-Xin Jiang, Yang Mo, Jianping Shen. 623-626 [doi]
• CSECU-DSG at SemEval-2021 Task 1: Fusion of Transformer Models for Lexical Complexity PredictionAbdul Aziz, Md. Akram Hossain, Abu Nowshed Chy. 627-631 [doi]
• CLULEX at SemEval-2021 Task 1: A Simple System Goes a Long WayGreta Smolenska, Peter Kolb, Sinan Tang, Mironas Bitinis, Héctor Hernández, Elin Asklöv. 632-639 [doi]
• RS_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity PredictionRegina Stodden, Gayatri Venugopal. 640-649 [doi]
• UNBNLP at SemEval-2021 Task 1: Predicting lexical complexity with masked language models and character-level encodersMilton King, Ali Hakimi Parizi, Samin Fakharian, Paul Cook. 650-654 [doi]
• ANDI at SemEval-2021 Task 1: Predicting complexity in context using distributional models, behavioural norms, and lexical resourcesArmand Rotaru. 655-660 [doi]
• JUST-BLUE at SemEval-2021 Task 1: Predicting Lexical Complexity using BERT and RoBERTa Pre-trained Language ModelsTuqa Bani Yaseen, Qusai Ismail, Sarah Al-Omari, Eslam Al-Sobh, Malak Abdullah. 661-666 [doi]
• BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly ModelsAadil Islam, Weicheng Ma, Soroush Vosoughi. 667-677 [doi]
• cs60075_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corporaAbhilash Nandy, Sayantan Adak, Tanurima Halder, Sai Mahesh Pokala. 678-682 [doi]
• C3SL at SemEval-2021 Task 1: Predicting Lexical Complexity of Words in Specific Contexts with Sentence EmbeddingsRaul Almeida, Hegler Tissot, Marcos Didonet Del Fabro. 683-687 [doi]
• Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word EmbeddingsErik Rozi, Niveditha Iyer, Gordon Chi, Enok Choe, Kathy J. Lee, Kevin Liu, Patrick Liu, Zander Lack, Jillian Tang, Ethan A. Chi. 688-693 [doi]
• archer at SemEval-2021 Task 1: Contextualising Lexical ComplexityIrene Russo. 694-699 [doi]
• katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in EnglishKatja Voskoboinik. 700-705 [doi]
• GX at SemEval-2021 Task 2: BERT with Lemma Information for MCL-WiC TaskWanying Xie. 706-712 [doi]
• PALI at SemEval-2021 Task 2: Fine-Tune XLM-RoBERTa for Word in Context DisambiguationShu Yi Xie, Jian Ma, Haiqin Yang, Lian-Xin Jiang, Yang Mo, Jianping Shen. 713-718 [doi]
• hub at SemEval-2021 Task 2: Word Meaning Similarity Prediction Model Based on RoBERTa and Word FrequencyBo Huang, Yang Bai, Xiaobing Zhou. 719-723 [doi]
• Lotus at SemEval-2021 Task 2: Combination of BERT and Paraphrasing for English Word Sense DisambiguationNiloofar Ranjbar, Hossein Zeinali. 724-729 [doi]
• Cambridge at SemEval-2021 Task 2: Neural WiC-Model with Data Augmentation and Exploration of RepresentationZheng Yuan, David Strohmaier. 730-737 [doi]
• UoB_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense DisambiguationWei Li, Harish Tayyar Madabushi, Mark Lee. 738-742 [doi]
• PAW at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation : Exploring Cross Lingual Transfer, Augmentations and Adversarial TrainingHarsh Goyal, Aadarsh Singh, Priyanshu Kumar. 743-747 [doi]
• LU-BZU at SemEval-2021 Task 2: Word2Vec and Lemma2Vec performance in Arabic Word-in-Context disambiguationMoustafa Al-Hajj, Mustafa Jarrar. 748-755 [doi]
• GlossReader at SemEval-2021 Task 2: Reading Definitions Improves Contextualized Word EmbeddingsMaxim Rachinskiy, Nikolay Arefyev. 756-762 [doi]
• UAlberta at SemEval-2021 Task 2: Determining Sense Synonymy via TranslationsBradley Hauer, Hongchang Bao, Arnob Mallik, Grzegorz Kondrak. 763-770 [doi]
• TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context DisambiguationHansi Hettiarachchi, Tharindu Ranasinghe. 771-779 [doi]
• LIORI at SemEval-2021 Task 2: Span Prediction and Binary Classification approaches to Word-in-Context DisambiguationAdis Davletov, Nikolay Arefyev, Denis Gordeev, Alexey Rey. 780-786 [doi]
• FII_CROSS at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context DisambiguationCiprian Bodnar, Andrada Tapuc, Cosmin Pintilie, Daniela Gîfu, Diana Trandabat. 787-792 [doi]
• XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract MeaningYuxin Jiang, Ziyi Shou, Qijun Wang, Hao Wu, Fangzhen Lin. 793-798 [doi]
• UoR at SemEval-2021 Task 4: Using Pre-trained BERT Token Embeddings for Question Answering of Abstract MeaningThanet Markchom, Huizhi Liang. 799-804 [doi]
• Noobs at Semeval-2021 Task 4: Masked Language Modeling for abstract answer predictionShikhar Shukla, Sarthak, Karm Veer Arya. 805-809 [doi]
• ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract MeaningXin Xie, Xiangnan Chen, Xiang Chen, Yong Wang, Ningyu Zhang, Shumin Deng, Huajun Chen. 810-819 [doi]
• PINGAN Omini-Sinitic at SemEval-2021 Task 4: Reading Comprehension of Abstract MeaningYe Wang, Yanmeng Wang, Haijun Zhu, Bo Zeng, Zhenghong Hao, Shaojun Wang, Jing Xiao. 820-826 [doi]
• NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading ComprehensionZhixiang Chen, Yikun Lei, Pai Liu, Guibing Guo. 827-832 [doi]
• WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic SpansTharindu Ranasinghe, Diptanu Sarkar, Marcos Zampieri, Alexander G. Ororbia. 833-840 [doi]
• YNU-HPCC at SemEval-2021 Task 5: Using a Transformer-based Model with Auxiliary Information for Toxic Span DetectionRuiJun Chen, Jin Wang, Xuejie Zhang. 841-845 [doi]
• UIT-ISE-NLP at SemEval-2021 Task 5: Toxic Spans Detection with BiLSTM-CRF and ToxicBERT Comment ClassificationSon T. Luu, Ngan Nguyen. 846-851 [doi]
• GHOST at SemEval-2021 Task 5: Is explanation all you need?Kamil Plucinski, Hanna Klimczak. 852-859 [doi]
• GoldenWind at SemEval-2021 Task 5: Orthrus - An Ensemble Approach to Identify ToxicityMarco Palomino, Dawid Grad, James Bedwell. 860-864 [doi]
• LISAC FSDM USMBA at SemEval-2021 Task 5: Tackling Toxic Spans Detection Challenge with Supervised SpanBERT-based Model and Unsupervised LIME-based ModelAbdessamad Benlahbib, Ahmed Alami, Hamza Alami. 865-869 [doi]
• HITMI&T at SemEval-2021 Task 5: Integrating Transformer and CRF for Toxic Spans DetectionChenyi Wang, Tianshu Liu, Tiejun Zhao. 870-874 [doi]
• AStarTwice at SemEval-2021 Task 5: Toxic Span Detection Using RoBERTa-CRF, Domain Specific Pre-Training and Self-TrainingThakur Ashutosh Suman, Abhinav Jain. 875-880 [doi]
• NLP_UIOWA at Semeval-2021 Task 5: Transferring Toxic Sets to Tag Toxic SpansJonathan Rusert. 881-887 [doi]
• S-NLP at SemEval-2021 Task 5: An Analysis of Dual Networks for Sequence TaggingViet Anh Nguyen, Tam Minh Nguyen, Huy Dao Quang, Quang Pham Huu. 888-897 [doi]
• UAntwerp at SemEval-2021 Task 5: Spans are Spans, stacking a binary word level approach to toxic span detectionBen Burtenshaw, Mike Kestemont. 898-903 [doi]
• hub at SemEval-2021 Task 5: Toxic Span Detection Based on Word-Level ClassificationBo Huang, Yang Bai, Xiaobing Zhou. 904-908 [doi]
• Sefamerve ARGE at SemEval-2021 Task 5: Toxic Spans Detection Using Segmentation Based 1-D Convolutional Neural Network ModelSelman Delil, Birol Kuyumcu, Cüneyt Aksakalli. 909-912 [doi]
• MIPT-NSU-UTMN at SemEval-2021 Task 5: Ensembling Learning with Pre-trained Language Models for Toxic Spans DetectionMikhail Kotyushev, Anna Glazkova, Dmitry Morozov. 913-918 [doi]
• UIT-E10dot3 at SemEval-2021 Task 5: Toxic Spans Detection with Named Entity Recognition and Question-Answering ApproachesPhu Gia Hoang, Luan Thanh Nguyen, Kiet Van Nguyen. 919-926 [doi]
• SkoltechNLP at SemEval-2021 Task 5: Leveraging Sentence-level Pre-training for Toxic Span DetectionDavid Dale, Igor Markov, Varvara Logacheva, Olga Kozlova, Nikita Semenov, Alexander Panchenko. 927-934 [doi]
• Entity at SemEval-2021 Task 5: Weakly Supervised Token Labelling for Toxic Spans DetectionVaibhav Jain, Mina Naghshnejad. 935-940 [doi]
• BennettNLP at SemEval-2021 Task 5: Toxic Spans Detection using Stacked Embedding Powered Toxic Entity RecognizerHarsh Kataria, Ambuje Gupta, Vipul Mishra. 941-947 [doi]
• UoT-UWF-PartAI at SemEval-2021 Task 5: Self Attention Based Bi-GRU with Multi-Embedding Representation for Toxicity HighlighterHamed Babaei Giglou, Taher Rahgooy, Mostafa Rahgouy, Jafar Razmara. 948-952 [doi]
• YoungSheldon at SemEval-2021 Task 5: Fine-tuning Pre-trained Language Models for Toxic Spans Detection using Token classification ObjectiveMayukh Sharma, Ilanthenral Kandasamy, W. B. Vasantha. 953-959 [doi]
• HLE-UPC at SemEval-2021 Task 5: Multi-Depth DistilBERT for Toxic Spans DetectionRafel Palliser-Sans, Albert Rial-Farràs. 960-966 [doi]
• Lone Pine at SemEval-2021 Task 5: Fine-Grained Detection of Hate Speech Using BERToxicYakoob Khan, Weicheng Ma, Soroush Vosoughi. 967-973 [doi]
• SRPOL DIALOGUE SYSTEMS at SemEval-2021 Task 5: Automatic Generation of Training Data for Toxic Spans DetectionMichal Satlawa, Katarzyna Zamlynska, Jaroslaw Piersa, Joanna Kolis, Klaudia Firlag, Katarzyna Beksa, Zuzanna Bordzicka, Christian Goltz, Pawel Bujnowski, Piotr Andruszkiewicz. 974-983 [doi]
• SINAI at SemEval-2021 Task 5: Combining Embeddings in a BiLSTM-CRF model for Toxic Spans DetectionFlor Miriam Plaza del Arco, Pilar López-Úbeda, Luis Alfonso Ureña López, María-Teresa Martín Valdivia. 984-989 [doi]
• CSECU-DSG at SemEval-2021 Task 5: Leveraging Ensemble of Sequence Tagging Models for Toxic Spans DetectionTashin Hossain, Jannatun Naim, Fareen Tasneem, Radiathun Tasnia, Abu Nowshed Chy. 990-994 [doi]
• UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble ModelsAlireza Salemi, Nazanin Sabri, Emad Kebriaei, Behnam Bahrak, Azadeh Shakery. 995-1002 [doi]
• macech at SemEval-2021 Task 5: Toxic Spans DetectionMaggie Cech. 1003-1008 [doi]
• LZ1904 at SemEval-2021 Task 5: Bi-LSTM-CRF for Toxic Span Detection using Pretrained Word EmbeddingLiang Zou, Wen Li. 1009-1014 [doi]
• LIIR at SemEval-2021 task 6: Detection of Persuasion Techniques In Texts and Images using CLIP featuresErfan Ghadery, Damien Sileo, Marie-Francine Moens. 1015-1019 [doi]
• AIMH at SemEval-2021 Task 6: Multimodal Classification Using an Ensemble of Transformer ModelsNicola Messina, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato. 1020-1026 [doi]
• HOMADOS at SemEval-2021 Task 6: Multi-Task Learning for Propaganda DetectionKonrad Kaczynski, Piotr Przybyla. 1027-1031 [doi]
• 1213Li at SemEval-2021 Task 6: Detection of Propaganda with Multi-modal Attention and Pre-trained ModelsPeiguang Li, Xuan Li, Xian Sun. 1032-1036 [doi]
• NLyticsFKIE at SemEval-2021 Task 6: Detection of Persuasion Techniques In Texts And ImagesAlbert Pritzkau. 1037-1044 [doi]
• YNU-HPCC at SemEval-2021 Task 6: Combining ALBERT and Text-CNN for Persuasion Detection in Texts and ImagesXingyu Zhu 0006, Jin Wang, Xuejie Zhang. 1045-1050 [doi]
• LT3 at SemEval-2021 Task 6: Using Multi-Modal Compact Bilinear Pooling to Combine Visual and Textual Understanding in MemesPranaydeep Singh, Els Lefever. 1051-1055 [doi]
• FPAI at SemEval-2021 Task 6: BERT-MRC for Propaganda Techniques DetectionXiaolong Hou, Junsong Ren, Gang Rao, Lianxin Lian, Zhihao Ruan, Yang Mo, Jianping Shen. 1056-1060 [doi]
• NLPIITR at SemEval-2021 Task 6: RoBERTa Model with Data Augmentation for Persuasion Techniques DetectionVansh Gupta, Raksha Sharma. 1061-1067 [doi]
• LeCun at SemEval-2021 Task 6: Detecting Persuasion Techniques in Text Using Ensembled Pretrained Transformers and Data AugmentationDia Abujaber, Ahmed Qarqaz, Malak Abdullah. 1068-1074 [doi]
• Volta at SemEval-2021 Task 6: Towards Detecting Persuasive Texts and Images using Textual and Multimodal EnsembleKshitij Gupta, Devansh Gautam, Radhika Mamidi. 1075-1081 [doi]
• MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal FusionJunfeng Tian, Min Gui, Chenliang Li, Ming Yan, Wenming Xiao. 1082-1087 [doi]
• CSECU-DSG at SemEval-2021 Task 6: Orchestrating Multimodal Neural Architectures for Identifying Persuasion Techniques in Texts and ImagesTashin Hossain, Jannatun Naim, Fareen Tasneem, Radiathun Tasnia, Abu Nowshed Chy. 1088-1095 [doi]
• UMUTeam at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Linguistic Features and Word EmbeddingsJosé Antonio García-Díaz, Rafael Valencia-García. 1096-1101 [doi]
• ES-JUST at SemEval-2021 Task 7: Detecting and Rating Humor and Offensive Text Using Deep LearningEmran Al-Bashabsheh, Sanaa Abu Alasal. 1102-1107 [doi]
• Tsia at SemEval-2021 Task 7: Detecting and Rating Humor and OffenseZhengyi Guan, Xiaobing Zhou. 1108-1113 [doi]
• DLJUST at SemEval-2021 Task 7: Hahackathon: Linking Humor and OffenseHani Al-Omari, Isra'a AbdulNabi, Rehab Duwairi. 1114-1119 [doi]
• Gulu at SemEval-2021 Task 7: Detecting and Rating Humor and OffenseMaoqin Yang. 1120-1124 [doi]
• DUTH at SemEval-2021 Task 7: Is Conventional Machine Learning for Humorous and Offensive Tasks enough in 2021?Alexandros Karasakalidis, Dimitrios Effrosynidis, Avi Arampatzis. 1125-1129 [doi]
• DeepBlueAI at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Stacking Diverse Language Model-Based MethodsBingyan Song, Chunguang Pan, Shengguang Wang, Zhipeng Luo. 1130-1134 [doi]
• CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and OffenseKabil Essefar, Abdellah El Mekki, Abdelkader El Mahdaouy, Nabil El Mamoun, Ismail Berrada. 1135-1140 [doi]
• hub at SemEval-2021 Task 7: Fusion of ALBERT and Word Frequency Information Detecting and Rating Humor and OffenseBo Huang, Yang Bai. 1141-1145 [doi]
• YoungSheldon at SemEval-2021 Task 7: Fine-tuning Is All You NeedMayukh Sharma, Ilanthenral Kandasamy, W. B. Vasantha. 1146-1152 [doi]
• MagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial TrainingJian Ma, Shu Yi Xie, Haiqin Yang, Lian-Xin Jiang, Mengyuan Zhou, Xiaoyi Ruan, Yang Mo. 1153-1159 [doi]
• UPB at SemEval-2021 Task 7: Adversarial Multi-Task Learning for Detecting and Rating Humor and OffenseRazvan-Alexandru Smadu, Dumitru-Clementin Cercel, Mihai Dascalu. 1160-1168 [doi]
• Team_KGP at SemEval-2021 Task 7: A Deep Neural System to Detect Humor and Offense with Their Ratings in the Text DataAnik Mondal, Raksha Sharma. 1169-1174 [doi]
• ZYJ at SemEval-2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense with ALBERT-Based ModelYingjia Zhao, Xin Tao. 1175-1178 [doi]
• UoR at SemEval-2021 Task 7: Utilizing Pre-trained DistilBERT Model and Multi-scale CNN for Humor DetectionZehao Liu, Carl Haines, Huizhi Liang. 1179-1184 [doi]
• TECHSSN at SemEval-2021 Task 7: Humor and Offense detection and classification using ColBERT embeddingsRajalakshmi Sivanaiah, Angel Deborah S, S. Milton Rajendram, T. T. Mirnalinee, Abrit Pal Singh, Aviansh Gupta, Ayush Nanda. 1185-1189 [doi]
• Amherst685 at SemEval-2021 Task 7: Joint Modeling of Classification and Regression for Humor and OffenseBrian Zylich, Akshay Gugnani, Gabriel Brookman, Nicholas Samoray. 1190-1195 [doi]
• DuluthNLP at SemEval-2021 Task 7: Fine-Tuning RoBERTa Model for Humor Detection and Offense RatingSamuel Akrah. 1196-1203 [doi]
• CSECU-DSG at SemEval-2021 Task 7: Detecting and Rating Humor and Offense Employing TransformersAfrin Sultana, Nabila Ayman, Abu Nowshed Chy. 1204-1208 [doi]
• RedwoodNLP at SemEval-2021 Task 7: Ensembled Pretrained and Lightweight Models for Humor DetectionNathan Chi, Ryan Chi. 1209-1214 [doi]
• EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and EnsemblesChandan Kumar Pandey, Chirag Singh, Karan Mangla. 1215-1220 [doi]
• IIITH at SemEval-2021 Task 7: Leveraging transformer-based humourous and offensive text detection architectures using lexical and hurtlex features and task adaptive pretrainingTathagata Raha, Ishan Sanjeev Upadhyay, Radhika Mamidi, Vasudeva Varma. 1221-1225 [doi]
• FII FUNNY at SemEval-2021 Task 7: HaHackathon: Detecting and rating Humor and OffenseMihai Samson, Daniela Gîfu. 1226-1231 [doi]
• Counts@IITK at SemEval-2021 Task 8: SciBERT Based Entity And Semantic Relation Extraction For Scientific DataAkash Gangwar, Sabhay Jain, Shubham Sourav, Ashutosh Modi. 1232-1238 [doi]
• CONNER: A Cascade Count and Measurement Extraction Tool for Scientific DiscourseJiarun Cao, Yuejia Xiang, Yunyan Zhang, Zhiyuan Qi, Xi Chen, Yefeng Zheng. 1239-1244 [doi]
• Stanford MLab at SemEval-2021 Task 8: 48 Hours Is All You NeedPatrick Liu, Niveditha Iyer, Erik Rozi, Ethan A. Chi. 1245-1248 [doi]
• LIORI at SemEval-2021 Task 8: Ask Transformer for measurementsAdis Davletov, Denis Gordeev, Nikolay Arefyev, Emil T. Davletov. 1249-1254 [doi]
• Sattiy at SemEval-2021 Task 9: An Ensemble Solution for Statement Verification and Evidence Finding with TablesXiaoyi Ruan, Meizhi Jin, Jian Ma, Haiqin Yang, Lian-Xin Jiang, Yang Mo, Mengyuan Zhou. 1255-1261 [doi]
• Volta at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables using TAPAS and Transfer LearningDevansh Gautam, Kshitij Gupta, Manish Shrivastava 0001. 1262-1270 [doi]
• KaushikAcharya at SemEval-2021 Task 9: Candidate Generation for Fact Verification over TablesKaushik Acharya. 1271-1275 [doi]
• AttesTable at SemEval-2021 Task 9: Extending Statement Verification with Tables for Unknown Class, and Semantic Evidence FindingHarshit Varma, Aadish Jain, Pratik Ratadiya, Abhishek Rathi. 1276-1282 [doi]
• MedAI at SemEval-2021 Task 10: Negation-aware Pre-training for Source-free Negation Detection Domain AdaptationJinquan Sun, Qi Zhang, Yu Wang, Lei Zhang. 1283-1288 [doi]
• YNU-HPCC at SemEval-2021 Task 10: Using a Transformer-based Source-Free Domain Adaptation Model for Semantic ProcessingZhewen Yu, Jin Wang, Xuejie Zhang. 1289-1294 [doi]
• ECNUICA at SemEval-2021 Task 11: Rule based Information Extraction PipelineJiaju Lin, Jing Ling, Zhiwei Wang, Jiawei Liu, Qin Chen, Liang He. 1295-1302 [doi]
• UOR at SemEval-2021 Task 12: On Crowd Annotations; Learning with Disagreements to optimise crowd truthEmmanuel Osei-Brefo, Thanet Markchom, Huizhi Liang. 1303-1309 [doi]