---
title: "SemEval 2021 — all accepted papers — the most cited first"
description: "All 186 papers of SemEval 2021 · page 1 of 2, the most cited first; those with our summary link to it."
canonical: "https://research.elamur.ai/free/en/venues/semeval/2021/papers/"
---
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# SemEval 2021: all accepted papers

186 papers · page 1 of 2 · the most cited first · [← SemEval 2021](https://md.research.elamur.ai/free/en/venues/semeval/2021/)

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1. **[SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images](https://md.research.elamur.ai/free/en/papers/dimitrov-2021-semeval-2021-task-6-detection/)**Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar et al.*88 citations · our summary*
2. **[SemEval-2021 Task 5: Toxic Spans Detection](https://md.research.elamur.ai/free/en/papers/pavlopoulos-2021-semeval-2021-task-5-toxic/)**John Pavlopoulos, Jeffrey Sorensen, Léo Laugier et al.*80 citations · our summary*
3. **[SemEval 2021 Task 7: HaHackathon, Detecting and Rating Humor and Offense](https://md.research.elamur.ai/free/en/papers/meaney-2021-semeval-2021-task-7-hahackathon/)**J. A. Meaney, Steven Wilson, Luis Chiruzzo et al.*75 citations · our summary*
4. **[SemEval-2021 Task 1: Lexical Complexity Prediction](https://md.research.elamur.ai/free/en/papers/shardlow-2021-semeval-2021-task-1-lexical/)**Matthew Shardlow, Richard Evans, Gustavo Henrique Paetzold et al.*59 citations · our summary*
5. **[SemEval-2021 Task 12: Learning with Disagreements ↗](https://aclanthology.org/2021.semeval-1.41/)**Alexandra Uma, Tommaso Fornaciari, Anca Dumitrache et al.*39 citations*
6. **[SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) ↗](https://aclanthology.org/2021.semeval-1.3/)**Federico Martelli, Najla Kalach, Gabriele Tola et al.*39 citations*
7. **[SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS) ↗](https://aclanthology.org/2021.semeval-1.39/)**Nancy X. R. Wang, Diwakar Mahajan, Marina Danilevsky et al.*36 citations*
8. **[SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge Graph ↗](https://aclanthology.org/2021.semeval-1.44/)**Jennifer D'Souza, Sören Auer, Ted Pedersen*29 citations*
9. **[SemEval-2021 Task 8: MeasEval – Extracting Counts and Measurements and their Related Contexts ↗](https://aclanthology.org/2021.semeval-1.38/)**Corey Harper, Jessica Cox, Curt Kohler et al.*27 citations*
10. **[JUST-BLUE at SemEval-2021 Task 1: Predicting Lexical Complexity using BERT and RoBERTa Pre-trained Language Models ↗](https://aclanthology.org/2021.semeval-1.85/)**Tuqa Bani Yaseen, Qusai Ismail, Sarah Al-Omari et al.*19 citations*
11. **[MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion ↗](https://aclanthology.org/2021.semeval-1.150/)**Junfeng Tian, Min Gui, Chenliang Li et al.*19 citations*
12. **[HITSZ-HLT at SemEval-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection ↗](https://aclanthology.org/2021.semeval-1.63/)**Qinglin Zhu, Zijie Lin, Yice Zhang et al.*17 citations*
13. **[SarcasmDet at SemEval-2021 Task 7: Detect Humor and Offensive based on Demographic Factors using RoBERTa Pre-trained Model ↗](https://aclanthology.org/2021.semeval-1.64/)**Dalya Faraj, Malak Abdullah*15 citations*
14. **[WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans ↗](https://aclanthology.org/2021.semeval-1.111/)**Tharindu Ranasinghe, Diptanu Sarkar, Marcos Zampieri et al.*14 citations*
15. **[SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning ↗](https://aclanthology.org/2021.semeval-1.4/)**Boyuan Zheng, Xiaoyu Yang, Yu-Ping Ruan et al.*13 citations*
16. **[Volta at SemEval-2021 Task 6: Towards Detecting Persuasive Texts and Images using Textual and Multimodal Ensemble ↗](https://aclanthology.org/2021.semeval-1.149/)**Kshitij Gupta, Devansh Gautam, Radhika Mamidi*13 citations*
17. **[Alpha at SemEval-2021 Task 6: Transformer Based Propaganda Classification ↗](https://aclanthology.org/2021.semeval-1.8/)**Zhida Feng, Jiji Tang, Jiaxiang Liu et al.*12 citations*
18. **[NLRG at SemEval-2021 Task 5: Toxic Spans Detection Leveraging BERT-based Token Classification and Span Prediction Techniques ↗](https://aclanthology.org/2021.semeval-1.27/)**Gunjan Chhablani, Abheesht Sharma, Harshit Pandey et al.*12 citations*
19. **[UIUC_BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP Contributions ↗](https://aclanthology.org/2021.semeval-1.45/)**Haoyang Liu, M. Janina Sarol, Halil Kilicoglu*12 citations*
20. **[CONNER: A Cascade Count and Measurement Extraction Tool for Scientific Discourse ↗](https://aclanthology.org/2021.semeval-1.176/)**Jiarun Cao, Yuejia Xiang, Yunyan Zhang et al.*11 citations*
21. **[AStarTwice at SemEval-2021 Task 5: Toxic Span Detection Using RoBERTa-CRF, Domain Specific Pre-Training and Self-Training ↗](https://aclanthology.org/2021.semeval-1.118/)**Thakur Ashutosh Suman, Abhinav Jain*10 citations*
22. **[Alejandro Mosquera at SemEval-2021 Task 1: Exploring Sentence and Word Features for Lexical Complexity Prediction ↗](https://aclanthology.org/2021.semeval-1.68/)**Alejandro Mosquera*10 citations*
23. **[DeepBlueAI at SemEval-2021 Task 1: Lexical Complexity Prediction with A Deep Ensemble Approach ↗](https://aclanthology.org/2021.semeval-1.72/)**Chunguang Pan, Bingyan Song, Shengguang Wang et al.*10 citations*
24. **[LIORI at SemEval-2021 Task 8: Ask Transformer for measurements ↗](https://aclanthology.org/2021.semeval-1.178/)**Adis Davletov, Denis Gordeev, Nikolay Arefyev et al.*10 citations*
25. **[LeCun at SemEval-2021 Task 6: Detecting Persuasion Techniques in Text Using Ensembled Pretrained Transformers and Data Augmentation ↗](https://aclanthology.org/2021.semeval-1.148/)**Dia Abujaber, Ahmed Qarqaz, Malak A. Abdullah*10 citations*
26. **[Counts@IITK at SemEval-2021 Task 8: SciBERT Based Entity And Semantic Relation Extraction For Scientific Data ↗](https://aclanthology.org/2021.semeval-1.175/)**Akash Gangwar, Sabhay Jain, Shubham Sourav et al.*9 citations*
27. **[LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction ↗](https://aclanthology.org/2021.semeval-1.67/)**Abhinandan Tejalkumar Desai, Kai North, Marcos Zampieri et al.*9 citations*
28. **[PALI at SemEval-2021 Task 2: Fine-Tune XLM-RoBERTa for Word in Context Disambiguation ↗](https://aclanthology.org/2021.semeval-1.93/)**Shuyi Xie, Jian Ma, Haiqin Yang et al.*9 citations*
29. **[SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing ↗](https://aclanthology.org/2021.semeval-1.42/)**Egoitz Laparra, Xin Su, Yiyun Zhao et al.*9 citations*
30. **[TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training ↗](https://aclanthology.org/2021.semeval-1.51/)**Thomas Müller, Julian Eisenschlos, Syrine Krichene*9 citations*
31. **[TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation ↗](https://aclanthology.org/2021.semeval-1.102/)**Hansi Hettiarachchi, Tharindu Ranasinghe*9 citations*
32. **[UIT-ISE-NLP at SemEval-2021 Task 5: Toxic Spans Detection with BiLSTM-CRF and ToxicBERT Comment Classification ↗](https://aclanthology.org/2021.semeval-1.113/)**Son T. Luu, Ngan Nguyen*9 citations*
33. **[AIMH at SemEval-2021 Task 6: Multimodal Classification Using an Ensemble of Transformer Models ↗](https://aclanthology.org/2021.semeval-1.140/)**Nicola Messina, Fabrizio Falchi, Claudio Gennaro et al.*8 citations*
34. **[GoldenWind at SemEval-2021 Task 5: Orthrus - An Ensemble Approach to Identify Toxicity ↗](https://aclanthology.org/2021.semeval-1.115/)**Marco Palomino, Dawid Grad, James Bedwell*8 citations*
35. **[HamiltonDinggg at SemEval-2021 Task 5: Investigating Toxic Span Detection using RoBERTa Pre-training ↗](https://aclanthology.org/2021.semeval-1.31/)**Huiyang Ding, David Jurgens*8 citations*
36. **[IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection ↗](https://aclanthology.org/2021.semeval-1.24/)**Archit Bansal, Abhay Kaushik, Ashutosh Modi*8 citations*
37. **[LU-BZU at SemEval-2021 Task 2: Word2Vec and Lemma2Vec performance in Arabic Word-in-Context disambiguation ↗](https://aclanthology.org/2021.semeval-1.99/)**Moustafa Al-Hajj, Mustafa Jarrar*8 citations*
38. **[CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and Offense ↗](https://aclanthology.org/2021.semeval-1.159/)**Kabil Essefar, Abdellah El Mekki, Abdelkader El Mahdaouy et al.*7 citations*
39. **[LIORI at SemEval-2021 Task 2: Span Prediction and Binary Classification approaches to Word-in-Context Disambiguation ↗](https://aclanthology.org/2021.semeval-1.103/)**Adis Davletov, Nikolay Arefyev, Denis Gordeev et al.*7 citations*
40. **[S-NLP at SemEval-2021 Task 5: An Analysis of Dual Networks for Sequence Tagging ↗](https://aclanthology.org/2021.semeval-1.120/)**Viet Anh Nguyen, Tam Minh Nguyen, Huy Quang Dao et al.*7 citations*
41. **[UIT-E10dot3 at SemEval-2021 Task 5: Toxic Spans Detection with Named Entity Recognition and Question-Answering Approaches ↗](https://aclanthology.org/2021.semeval-1.125/)**Phu Gia Hoang, Luan Thanh Nguyen, Kiet Nguyen*7 citations*
42. **[UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity Prediction ↗](https://aclanthology.org/2021.semeval-1.77/)**George-Eduard Zaharia, Dumitru-Clementin Cercel, Mihai Dascalu*7 citations*
43. **[1213Li at SemEval-2021 Task 6: Detection of Propaganda with Multi-modal Attention and Pre-trained Models ↗](https://aclanthology.org/2021.semeval-1.142/)**Peiguang Li, Xuan Li, Xian Sun*6 citations*
44. **[Cisco at SemEval-2021 Task 5: What's Toxic?: Leveraging Transformers for Multiple Toxic Span Extraction from Online Comments ↗](https://aclanthology.org/2021.semeval-1.29/)**Sreyan Ghosh, Sonal Kumar*6 citations*
45. **[DeepBlueAI at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Stacking Diverse Language Model-Based Methods ↗](https://aclanthology.org/2021.semeval-1.158/)**Bingyan Song, Chunguang Pan, Shengguang Wang et al.*6 citations*
46. **[ECNUICA at SemEval-2021 Task 11: Rule based Information Extraction Pipeline ↗](https://aclanthology.org/2021.semeval-1.185/)**Jiaju Lin, Jing Ling, Zhiwei Wang et al.*6 citations*
47. **[GHOST at SemEval-2021 Task 5: Is explanation all you need? ↗](https://aclanthology.org/2021.semeval-1.114/)**Kamil Pluciński, Hanna Klimczak*6 citations*
48. **[KnowGraph@IITK at SemEval-2021 Task 11: Building Knowledge Graph for NLP Research ↗](https://aclanthology.org/2021.semeval-1.57/)**Shashank Shailabh, Sajal Chaurasia, Ashutosh Modi*6 citations*
49. **[LT3 at SemEval-2021 Task 6: Using Multi-Modal Compact Bilinear Pooling to Combine Visual and Textual Understanding in Memes ↗](https://aclanthology.org/2021.semeval-1.145/)**Pranaydeep Singh, Els Lefever*6 citations*
50. **[Lone Pine at SemEval-2021 Task 5: Fine-Grained Detection of Hate Speech Using BERToxic ↗](https://aclanthology.org/2021.semeval-1.132/)**Yakoob Khan, Weicheng Ma, Soroush Vosoughi*6 citations*
51. **[NLP_UIOWA at Semeval-2021 Task 5: Transferring Toxic Sets to Tag Toxic Spans ↗](https://aclanthology.org/2021.semeval-1.119/)**Jonathan Rusert*6 citations*
52. **[OCHADAI-KYOTO at SemEval-2021 Task 1: Enhancing Model Generalization and Robustness for Lexical Complexity Prediction ↗](https://aclanthology.org/2021.semeval-1.2/)**Yuki Taya, Lis Kanashiro Pereira, Fei Cheng et al.*6 citations*
53. **[UAntwerp at SemEval-2021 Task 5: Spans are Spans, stacking a binary word level approach to toxic span detection ↗](https://aclanthology.org/2021.semeval-1.121/)**Ben Burtenshaw, Mike Kestemont*6 citations*
54. **[YNU-HPCC at SemEval-2021 Task 11: Using a BERT Model to Extract Contributions from NLP Scholarly Articles ↗](https://aclanthology.org/2021.semeval-1.58/)**Xinge Ma, Jin Wang, Xuejie Zhang*6 citations*
55. **[YNU-HPCC at SemEval-2021 Task 6: Combining ALBERT and Text-CNN for Persuasion Detection in Texts and Images ↗](https://aclanthology.org/2021.semeval-1.144/)**Xingyu Zhu, Jin Wang, Xuejie Zhang*6 citations*
56. **[CSECU-DSG at SemEval-2021 Task 6: Orchestrating Multimodal Neural Architectures for Identifying Persuasion Techniques in Texts and Images ↗](https://aclanthology.org/2021.semeval-1.151/)**Tashin Hossain, Jannatun Naim, Fareen Tasneem et al.*5 citations*
57. **[HLE-UPC at SemEval-2021 Task 5: Multi-Depth DistilBERT for Toxic Spans Detection ↗](https://aclanthology.org/2021.semeval-1.131/)**Rafel Palliser-Sans, Albert Rial-Farràs*5 citations*
58. **[INNOVATORS at SemEval-2021 Task-11: A Dependency Parsing and BERT-based model for Extracting Contribution Knowledge from Scientific Papers ↗](https://aclanthology.org/2021.semeval-1.61/)**Hardik Arora, Tirthankar Ghosal, Sandeep Kumar et al.*5 citations*
59. **[LISAC FSDM USMBA at SemEval-2021 Task 5: Tackling Toxic Spans Detection Challenge with Supervised SpanBERT-based Model and Unsupervised LIME-based Model ↗](https://aclanthology.org/2021.semeval-1.116/)**Abdessamad Benlahbib, Ahmed Alami, Hamza Alami*5 citations*
60. **[RG PA at SemEval-2021 Task 1: A Contextual Attention-based Model with RoBERTa for Lexical Complexity Prediction ↗](https://aclanthology.org/2021.semeval-1.79/)**Gang Rao, Maochang Li, Xiaolong Hou et al.*5 citations*
61. **[UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements as Multi-Turn Question Answering ↗](https://aclanthology.org/2021.semeval-1.65/)**Andrei-Marius Avram, George-Eduard Zaharia, Dumitru-Clementin Cercel et al.*5 citations*
62. **[UTFPR at SemEval-2021 Task 1: Complexity Prediction by Combining BERT Vectors and Classic Features ↗](https://aclanthology.org/2021.semeval-1.78/)**Gustavo Henrique Paetzold*5 citations*
63. **[UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words Model ↗](https://aclanthology.org/2021.semeval-1.25/)**Akbar Karimi, Leonardo Rossi, Andrea Prati*5 citations*
64. **[Zhestyatsky at SemEval-2021 Task 2: ReLU over Cosine Similarity for BERT Fine-tuning ↗](https://aclanthology.org/2021.semeval-1.17/)**Boris Zhestiankin, Maria Ponomareva*5 citations*
65. **[cs60075_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora ↗](https://aclanthology.org/2021.semeval-1.87/)**Abhilash Nandy, Sayantan Adak, Tanurima Halder et al.*5 citations*
66. **[ANDI at SemEval-2021 Task 1: Predicting complexity in context using distributional models, behavioural norms, and lexical resources ↗](https://aclanthology.org/2021.semeval-1.84/)**Armand Rotaru*4 citations*
67. **[BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly Models ↗](https://aclanthology.org/2021.semeval-1.86/)**Aadil Islam, Weicheng Ma, Soroush Vosoughi*4 citations*
68. **[BreakingBERT@IITK at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables ↗](https://aclanthology.org/2021.semeval-1.40/)**Aditya Jindal, Ankur Gupta, Jaya Srivastava et al.*4 citations*
69. **[CLaC-np at SemEval-2021 Task 8: Dependency DGCNN ↗](https://aclanthology.org/2021.semeval-1.48/)**Nihatha Lathiff, Pavel PK Khloponin, Sabine Bergler*4 citations*
70. **[CS-UM6P at SemEval-2021 Task 1: A Deep Learning Model-based Pre-trained Transformer Encoder for Lexical Complexity ↗](https://aclanthology.org/2021.semeval-1.73/)**Nabil El Mamoun, Abdelkader El Mahdaouy, Abdellah El Mekki et al.*4 citations*
71. **[CSECU-DSG at SemEval-2021 Task 1: Fusion of Transformer Models for Lexical Complexity Prediction ↗](https://aclanthology.org/2021.semeval-1.80/)**Abdul Aziz, MD. Akram Hossain, Abu Nowshed Chy*4 citations*
72. **[Duluth at SemEval-2021 Task 11: Applying DeBERTa to Contributing Sentence Selection and Dependency Parsing for Entity Extraction ↗](https://aclanthology.org/2021.semeval-1.60/)**Anna Martin, Ted Pedersen*4 citations*
73. **[HumorHunter at SemEval-2021 Task 7: Humor and Offense Recognition with Disentangled Attention ↗](https://aclanthology.org/2021.semeval-1.33/)**Yubo Xie, Junze Li, Pearl Pu*4 citations*
74. **[LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures ↗](https://aclanthology.org/2021.semeval-1.71/)**Yves Bestgen*4 citations*
75. **[LIIR at SemEval-2021 task 6: Detection of Persuasion Techniques In Texts and Images using CLIP features ↗](https://aclanthology.org/2021.semeval-1.139/)**Erfan Ghadery, Damien Sileo, Marie-Francine Moens*4 citations*
76. **[MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection ↗](https://aclanthology.org/2021.semeval-1.30/)**Zhen Wang, Hongjie Fan, Junfei Liu*4 citations*
77. **[NLyticsFKIE at SemEval-2021 Task 6: Detection of Persuasion Techniques In Texts And Images ↗](https://aclanthology.org/2021.semeval-1.143/)**Albert Pritzkau*4 citations*
78. **[Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings ↗](https://aclanthology.org/2021.semeval-1.89/)**Erik Rozi, Niveditha Iyer, Gordon Chi et al.*4 citations*
79. **[TECHSSN at SemEval-2021 Task 7: Humor and Offense detection and classification using ColBERT embeddings ↗](https://aclanthology.org/2021.semeval-1.167/)**Rajalakshmi Sivanaiah, Angel Deborah S, S Milton Rajendram et al.*4 citations*
80. **[The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation ↗](https://aclanthology.org/2021.semeval-1.56/)**Xin Su, Yiyun Zhao, Steven Bethard*4 citations*
81. **[UOR at SemEval-2021 Task 12: On Crowd Annotations; Learning with Disagreements to optimise crowd truth ↗](https://aclanthology.org/2021.semeval-1.186/)**Emmanuel Osei-Brefo, Thanet Markchom, Huizhi Liang*4 citations*
82. **[UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models ↗](https://aclanthology.org/2021.semeval-1.136/)**Alireza Salemi, Nazanin Sabri, Emad Kebriaei et al.*4 citations*
83. **[Volta at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables using TAPAS and Transfer Learning ↗](https://aclanthology.org/2021.semeval-1.180/)**Devansh Gautam, Kshitij Gupta, Manish Shrivastava*4 citations*
84. **[YoungSheldon at SemEval-2021 Task 5: Fine-tuning Pre-trained Language Models for Toxic Spans Detection using Token classification Objective ↗](https://aclanthology.org/2021.semeval-1.130/)**Mayukh Sharma, Ilanthenral Kandasamy, W.B. Vasantha*4 citations*
85. **[hub at SemEval-2021 Task 1: Fusion of Sentence and Word Frequency to Predict Lexical Complexity ↗](https://aclanthology.org/2021.semeval-1.75/)**Bo Huang, Yang Bai, Xiaobing Zhou*4 citations*
86. **[hub at SemEval-2021 Task 2: Word Meaning Similarity Prediction Model Based on RoBERTa and Word Frequency ↗](https://aclanthology.org/2021.semeval-1.94/)**Bo Huang, Yang Bai, Xiaobing Zhou*4 citations*
87. **[C3SL at SemEval-2021 Task 1: Predicting Lexical Complexity of Words in Specific Contexts with Sentence Embeddings ↗](https://aclanthology.org/2021.semeval-1.88/)**Raul Almeida, Hegler Tissot, Marcos Didonet Del Fabro*3 citations*
88. **[CLaC-BP at SemEval-2021 Task 8: SciBERT Plus Rules for MeasEval ↗](https://aclanthology.org/2021.semeval-1.49/)**Benjamin Therien, Parsa Bagherzadeh, Sabine Bergler*3 citations*
89. **[Cambridge at SemEval-2021 Task 1: An Ensemble of Feature-Based and Neural Models for Lexical Complexity Prediction ↗](https://aclanthology.org/2021.semeval-1.74/)**Zheng Yuan, Gladys Tyen, David Strohmaier*3 citations*
90. **[CompNA at SemEval-2021 Task 1: Prediction of lexical complexity analyzing heterogeneous features ↗](https://aclanthology.org/2021.semeval-1.69/)**Giuseppe Vettigli, Antonio Sorgente*3 citations*
91. **[Complex words identification using word-level features for SemEval-2020 Task 1 ↗](https://aclanthology.org/2021.semeval-1.11/)**Jenny A. Ortiz-Zambrano, Arturo Montejo-Ráez*3 citations*
92. **[DUTH at SemEval-2021 Task 7: Is Conventional Machine Learning for Humorous and Offensive Tasks enough in 2021? ↗](https://aclanthology.org/2021.semeval-1.157/)**Alexandros Karasakalidis, Dimitrios Effrosynidis, Avi Arampatzis*3 citations*
93. **[FII FUNNY at SemEval-2021 Task 7: HaHackathon: Detecting and rating Humor and Offense ↗](https://aclanthology.org/2021.semeval-1.174/)**Mihai Samson, Daniela Gifu*3 citations*
94. **[GlossReader at SemEval-2021 Task 2: Reading Definitions Improves Contextualized Word Embeddings ↗](https://aclanthology.org/2021.semeval-1.100/)**Maxim Rachinskiy, Nikolay Arefyev*3 citations*
95. **[IAPUCP at SemEval-2021 Task 1: Stacking Fine-Tuned Transformers is Almost All You Need for Lexical Complexity Prediction ↗](https://aclanthology.org/2021.semeval-1.14/)**Kervy Rivas Rojas, Fernando Alva-Manchego*3 citations*
96. **[IITK@LCP at SemEval-2021 Task 1: Classification for Lexical Complexity Regression Task ↗](https://aclanthology.org/2021.semeval-1.66/)**Neil Shirude, Sagnik Mukherjee, Tushar Shandhilya et al.*3 citations*
97. **[ITNLP at SemEval-2021 Task 11: Boosting BERT with Sampling and Adversarial Training for Knowledge Extraction ↗](https://aclanthology.org/2021.semeval-1.59/)**Genyu Zhang, Yu Su, Changhong He et al.*3 citations*
98. **[JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction ↗](https://aclanthology.org/2021.semeval-1.13/)**Chaya Liebeskind, Otniel Elkayam, Shmuel Liebeskind*3 citations*
99. **[KGP at SemEval-2021 Task 8: Leveraging Multi-Staged Language Models for Extracting Measurements, their Attributes and Relations ↗](https://aclanthology.org/2021.semeval-1.46/)**Neel Karia, Ayush Kaushal, Faraaz Mallick*3 citations*
100. **[LZ1904 at SemEval-2021 Task 5: Bi-LSTM-CRF for Toxic Span Detection using Pretrained Word Embedding ↗](https://aclanthology.org/2021.semeval-1.138/)**Liang Zou, Wen Li*3 citations*

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