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SemEval-2020 Program |
SemEval-2020 will be colocated with COLING 2020. All times shown are Central European Time (CET, UTC+1)
Proceedings in the ACL Anthology
(schedule updated: 3 Dec)
14:00-14:30
Opening remarks, Q&A for oral presentations
- #325 SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets
Parth Patwa, Gustavo Aguilar, Sudipta Kar, Suraj Pandey, Srinivas PYKL, Björn Gambäck, Tanmoy Chakraborty, Thamar Solorio and Amitava Das - #327 SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles
Giovanni Da San Martino, Alberto Barrón-Cedeño, Henning Wachsmuth, Rostislav Petrov and Preslav Nakov - #326 SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
Marcos Zampieri, Preslav Nakov, Sara Rosenthal, Pepa Atanasova, Georgi Karadzhov, Hamdy Mubarak, Leon Derczynski, Zeses Pitenis and Çağrı Çöltekin - #153 Kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification
Jiaxiang Liu, Xuyi Chen, Shikun Feng, Shuohuan Wang, Xuan Ouyang, Yu Sun, Zhengjie Huang and Weiyue Su - #305 ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether Self-Training Helps Them
Dawid Jurkiewicz, Łukasz Borchmann, Izabela Kosmala and Filip Graliński - #228 Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification Using Pre-trained Language Models
Shuohuan Wang, Jiaxiang Liu, Xuan Ouyang and Yu Sun
14:30-16:00
Poster session
- #7 Palomino-Ochoa at SemEval-2020 Task 9: Robust System Based on Transformer for Code-Mixed Sentiment Classification
Daniel Palomino and José Ochoa-Luna - #39 XLP at SemEval-2020 Task 9: Cross-lingual Models with Focal Loss for Sentiment Analysis of Code-Mixing Language
Yili Ma, Liang Zhao and Jie Hao - #78 ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text
Koustava Goswami, Priya Rani, Bharathi Raja Chakravarthi, Theodorus Fransen and John P. McCrae - #129 FII-UAIC at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using CNN
Lavinia Aparaschivei, Andrei Palihovici and Daniela Gîfu - #143 CS-Embed at SemEval-2020 Task 9: The Effectiveness of Code-switched Word Embeddings for Sentiment Analysis
Frances Adriana Laureano De Leon, Florimond Guéniat and Harish Tayyar Madabushi - #146 HPCC-YNU at SemEval-2020 Task 9: A Bilingual Vector Gating Mechanism for Sentiment Analysis of Code-Mixed Text
Jun Kong, Jin Wang and Xuejie Zhang - #290 HinglishNLP at SemEval-2020 Task 9: Fine-tuned Language Models for Hinglish Sentiment Detection
Meghana Bhange and Nirant Kasliwal - #307 IIITG-ADBU at SemEval-2020 Task 9: SVM for Sentiment Analysis of English-Hindi Code-Mixed Text
Arup Baruah, Kaushik Das, Ferdous Barbhuiya and Kuntal Dey - #37 CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection
Verena Blaschke, Maxim Korniyenko and Sam Tureski - #137 YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles
Jiaxu Dao, Jin Wang and Xuejie Zhang - #193 Aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning
Anton Chernyavskiy, Dmitry Ilvovsky and Preslav Nakov - #270 NTUAAILS at SemEval-2020 Task 11: Propaganda Detection and Classification with biLSTMs and ELMo
Anastasios Arsenos and Georgios Siolas - #300 Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles
Michael Kranzlein, Shabnam Behzad and Nazli Goharian - #304 NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification
Ilya Dimov, Vladislav Korzun and Ivan Smurov - #20 UHH-LT at SemEval-2020 Task 12: Fine-Tuning of Pre-Trained Transformer Networks for Offensive Language Detection
Gregor Wiedemann, Seid Muhie Yimam and Chris Biemann - #23 GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models
Davide Colla, Tommaso Caselli, Valerio Basile, Jelena Mitrović and Michael Granitzer - #44 FBK-DH at SemEval-2020 Task 12: Using Multi-channel BERT for Multilingual Offensive Language Detection
Camilla Casula, Alessio Palmero Aprosio, Stefano Menini and Sara Tonelli - #45 AdelaideCyC at SemEval-2020 Task 12: Ensemble of Classifiers for Offensive Language Detection in Social Media
Mahen Herath, Thushari Atapattu, Hoang Anh Dung, Christoph Treude and Katrina Falkner - #64 PRHLT-UPV at SemEval-2020 Task 12: BERT for Multilingual Offensive Language Detection
Gretel Liz De la Peña Sarracén and Paolo Rosso - #86 SINAI at SemEval-2020 Task 12: Offensive Language Identification Exploring Transfer Learning Models
Flor Miriam Plaza del Arco, M. Dolores Molina González, Alfonso Ureña-López and Maite Martin - #89 NUIG at SemEval-2020 Task 12: Pseudo Labelling for Offensive Content Classification
Shardul Suryawanshi, Mihael Arcan and Paul Buitelaar - #165 Team Oulu at SemEval-2020 Task 12: Multilingual Identification of Offensive Language, Type and Target of Twitter Post Using Translated Datasets
Md Saroar Jahan - #224 BhamNLP at SemEval-2020 Task 12: An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media
Abdullah I. Alharbi and Mark Lee - #254 IIITG-ADBU at SemEval-2020 Task 12: Comparison of BERT and BiLSTM in Detecting Offensive Language
Arup Baruah, Kaushik Das, Ferdous Barbhuiya and Kuntal Dey - #266 GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection
Sajad Sotudeh, Tong Xiang, Hao-Ren Yao, Sean MacAvaney, Eugene Yang, Nazli Goharian and Ophir Frieder - #273 PUM at SemEval-2020 Task 12: Aggregation of Transformer-based Models’ Features for Offensive Language Recognition
Piotr Janiszewski, Mateusz Skiba and Urszula Walińska - #286 Nova-Wang at SemEval-2020 Task 12: OffensEmblert: An Ensemble ofOffensive Language Classifiers
Susan Wang and Zita Marinho - #287 NLPDove at SemEval-2020 Task 12: Improving Offensive Language Detection with Cross-lingual Transfer
Hwijeen Ahn, Jimin Sun, Chan Young Park and Jungyun Seo - #291 ANDES at SemEval-2020 Task 12: A Jointly-trained BERT Multilingual Model for Offensive Language Detection
Juan Manuel Pérez, Aymé Arango and Franco Luque
Session Two: *SEM/SemEval keynote talk: Afra Alishahi
16:00-17:00
Grounded language learning, from sounds and images to meaning, Afra Alishahi, University of Tilburg
Abstract: Humans learn to understand speech from weak and noisy supervision: they manage to extract structure and meaning from speech by simply being exposed to utterances situated and grounded in their daily sensory experience. Emulating this remarkable skill has been the goal of numerous studies; however researchers have often used severely simplified settings where either the language input or the extralinguistic sensory input, or both, are small-scale and symbolically represented. I present a series of studies on modelling visually grounded language understanding. Using variations of recurrent neural networks to model the temporal nature of spoken language, we examine how form and meaning-based linguistic knowledge emerges from the input signal.
17:00-17:30
Q&A for oral presentations
- #315 SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky and Nina Tahmasebi - #323 SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment
Goran Glavaš, Ivan Vulić, Anna Korhonen and Simone Paolo Ponzetto - #316 SemEval-2020 Task 3: Graded Word Similarity in Context
Carlos Santos Armendariz, Matthew Purver, Senja Pollak, Nikola Ljubešić, Matej Ulčar, Ivan Vulić and Mohammad Taher Pilehvar - #74 DiaSense at SemEval-2020 Task 1: Modeling Sense Change via Pre-trained BERT Embeddings
Christin Beck - #209 BabelEnconding at SemEval-2020 Task 3: Contextual Similarity as a Combination of Multilingualism and Language Models
Lucas Rafael Costella Pessutto, Tiago de Melo, Viviane P. Moreira and Altigran da Silva - #13 Hitachi at SemEval-2020 Task 7: Stacking at Scale with Heterogeneous Language Models for Humor Recognition
Terufumi Morishita, Gaku Morio, Hiroaki Ozaki and Toshinori Miyoshi
(NOTE: Task 7 best paper presented in this session because of scheduling constraints)
17:30-19:00
Poster session
- #25 UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection
Andrey Kutuzov and Mario Giulianelli - #26 Discovery Team at SemEval-2020 Task 1: Context-sensitive Embeddings Not Always Better than Static for Semantic Change Detection
Matej Martinc, Syrielle Montariol, Elaine Zosa and Lidia Pivovarova - #127 SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change
Maurício Gruppi, Sibel Adali and Pin-Yu Chen - #140 SenseCluster at SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
Amaru Cuba Gyllensten, Evangelia Gogoulou, Ariel Ekgren and Magnus Sahlgren - #158 IMS at SemEval-2020 Task 1: How Low Can You Go? Dimensionality in Lexical Semantic Change Detection
Jens Kaiser, Dominik Schlechtweg, Sean Papay and Sabine Schulte im Walde - #238 GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering
Pierluigi Cassotti, Annalina Caputo, Marco Polignano and Pierpaolo Basile - #312 The UCD-Net System at SemEval-2020 Task 1: Temporal Referencing with Semantic Network Distances
Paul Nulty and David Lillis - #255 BMEAUT at SemEval-2020 Task 2: Lexical Entailment with Semantic Graphs
Ádám Kovács, Kinga Gémes, Andras Kornai and Gábor Recski - #199 MULTISEM at SemEval-2020 Task 3: Fine-tuning BERT for Lexical Meaning
Aina Garí Soler and Marianna Apidianaki - #201 UZH at SemEval-2020 Task 3: Combining BERT with WordNet Sense Embeddings to Predict Graded Word Similarity Changes
Li Tang - #218 BRUMS at SemEval-2020 Task 3: Contextualised Embeddings for Predicting the (Graded) Effect of Context in Word Similarity
Hansi Hettiarachchi and Tharindu Ranasinghe - #278 MineriaUNAM at SemEval-2020 Task 3: Predicting Contextual WordSimilarity Using a Centroid Based Approach and Word Embeddings
Helena Gomez-Adorno, Gemma Bel-Enguix, Jorge Reyes-Magaña, Benjamín Moreno, Ramón Casillas and Daniel Vargas
14:00-14:30
Q&A for oral presentations
- #322 SemEval-2020 Task 4: Commonsense Validation and Explanation
Cunxiang Wang, Shuailong Liang, Yili Jin, Yilong Wang, Xiaodan Zhu and Yue Zhang - #320 SemEval-2020 Task 5: Counterfactual Recognition
Xiaoyu Yang, Stephen Obadinma, Huasha Zhao, Qiong Zhang, Stan Matwin and Xiaodan Zhu - #318 SemEval-2020 Task 6: Definition Extraction from Free Text with the DEFT Corpus
Sasha Spala, Nicholas Miller, Franck Dernoncourt and Carl Dockhorn - #152 IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE
Luxi Xing, Yuqiang Xie, Yue Hu and Wei Peng - #164 HIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection
Xiao Ding, Dingkui Hao, Yuewei Zhang, Kuo Liao, Zhongyang Li, Bing Qin and Ting Liu - #241 Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification
Shelan Jeawak, Luis Espinosa-Anke and Steven Schockaert
14:30-16:00
Poster session
- #109 ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation
Qian Zhao, Siyu Tao, Jie Zhou, Linlin Wang, Xin Lin and Liang He - #114 UoR at SemEval-2020 Task 4: Pre-trained Sentence Transformer Models for Commonsense Validation and Explanation
Thanet Markchom, Bhuvana Dhruva, Chandresh Pravin and Huizhi Liang - #116 ANA at SemEval-2020 Task 4: MUlti-task learNIng for cOmmonsense reasoNing (UNION)
Anandh Konar, Chenyang Huang, Amine Trabelsi and Osmar Zaiane - #148 QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation System Based on Ensemble of Language Model
Liu Pai - #222 BUT-FIT at SemEval-2020 Task 4: Multilingual Commonsense
Josef Jon, Martin Fajcik, Martin Docekal and Pavel Smrz - #227 CUHK at SemEval-2020 Task 4: CommonSense Explanation, Reasoning and Prediction with Multi-task Learning
Hongru Wang, Xiangru Tang, Sunny Lai, Kwong Sak Leung, Jia Zhu, Gabriel Pui Cheong Fung and Kam-Fai Wong - #267 SWAGex at SemEval-2020 Task 4: Commonsense Explanation as Next Event Prediction
Wiem Ben Rim and Naoaki Okazaki - #309 Masked Reasoner at SemEval-2020 Task 4: Fine-Tuning RoBERTa for Commonsense Reasoning
Daming Lu - #117 BUT-FIT at SemEval-2020 Task 5: Automatic Detection of Counterfactual Statements with Deep Pre-trained Language Representation Models
Martin Fajcik, Josef Jon, Martin Docekal and Pavel Smrz - #265 CNRL at SemEval-2020 Task 5: Modelling Causal Reasoning in Language with Multi-Head Self-Attention Weights Based Counterfactual Detection
Rajaswa Patil and Veeky Baths - #275 CLaC at SemEval-2020 Task 5: Muli-task Stacked Bi-LSTMs
MinGyou Sung, Parsa Bagherzadeh and Sabine Bergler - #329 Yseop at SemEval-2020 Task 5: Cascaded BERT Language Model for Counterfactual Statement Analysis
Hanna Abi-Akl, Dominique Mariko and Estelle Labidurie - #167 ACNLP at SemEval-2020 Task 6: A Supervised Approach for Definition Extraction
Fabien Caspani, Pirashanth Ratnamogan, Mathis Linger and Mhamed Hajaiej - #281 Gorynych Transformer at SemEval-2020 Task 6: Multi-task Learning for Definition Extraction
Adis Davletov, Nikolay Arefyev, Alexander Shatilov, Denis Gordeev and Alexey Rey - #105 RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection
Ran Iwamoto and Masahiro Yukawa
Session Five: SemEval keynote talk: Jackie C.K. Cheung
16:00-17:00
From Discourse Structure to Semantics in Automatic Summarization, Jackie C.K. Cheung, McGill University
Abstract: The stereotypical discourse structure of a genre is often a good indicator of importance for content selection in automatic summarization. For example, the opening sentences of a news article usually form a good summary of it. However, relying on discourse structure could arguably be seen as a crutch on our way towards modelling the semantic content of source documents and the summaries. In this talk, I discuss the possibilities enabled by more explicitly thinking about semantics for neural abstractive summarization, with a focus on datasets and evaluations. I will discuss recent work on detecting and correcting factual inconsistencies in abstractive summaries. I will also emphasize the need for new summarization tasks that target semantic generalization and aggregation.
17:00-17:30
Q&A for oral presentations
- #317 SemEval-2020 Task 7: Assessing Humor in Edited News Headlines
Nabil Hossain, John Krumm, Michael Gamon and Henry Kautz - #324 SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!
Chhavi Sharma, Deepesh Bhageria, William Scott, Srinivas Pykl, Amitava Das, Tanmoy Chakraborty, Viswanath Pulabaigari and Björn Gambäck - #321 SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media
Amirreza Shirani, Franck Dernoncourt, Nedim Lipka, Paul Asente, Jose Echevarria and Thamar Solorio - #156 SESAM at SemEval-2020 Task 8: Investigating the Relationship between Image and Text in Sentiment Analysis of Memes
Lisa Bonheme and Marek Grzes - #196 IDS at SemEval-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize?
Jaeyoul Shin, Taeuk Kim and Sang-goo Lee
17:30-19:00
Poster session
- #21 Buhscitu at SemEval-2020 Task 7: Assessing Humour in Edited News Headlines Using Hand-Crafted Features and Online Knowledge Bases
Kristian Nørgaard Jensen, Nicolaj Filrup Rasmussen, Thai Wang, Marco Placenti and Barbara Plank - #33 YNU-HPCC at SemEval-2020 Task 7: Using an Ensemble BiGRU Model to Evaluate the Humor of Edited News Titles
Joseph Tomasulo, Jin Wang and Xuejie Zhang - #110 KDEhumor at SemEval-2020 Task 7: A Neural Network Model for Detecting Funniness in Dataset Humicroedit
Rida Miraj and Masaki Aono - #122 Hasyarasa at SemEval-2020 Task 7: Quantifying Humor as Departure from Expectedness<br# Ravi Theja Desetty, Ranit Chatterjee and Smita Ghaisas
- #174 SSN_NLP at SemEval-2020 Task 7: Detecting Funniness Level Using Traditional Learning with Sentence Embeddings
Kayalvizhi S, Thenmozhi D. and Aravindan Chandrabose - #177 JokeMeter at SemEval-2020 Task 7: Convolutional Humor
Martin Docekal, Martin Fajcik, Josef Jon and Pavel Smrz - #285 LRG at SemEval-2020 Task 7: Assessing the Ability of BERT and Derivative Models to Perform Short-Edits Based Humor Grading
Siddhant Mahurkar and Rajaswa Patil - #53 YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis
Li Yuan, Jin Wang and Xuejie Zhang - #65 PRHLT-UPV at SemEval-2020 Task 8: Study of Multimodal Techniques for Memes Analysis
Gretel Liz De la Peña Sarracén, Paolo Rosso and Anastasia Giachanou - #179 NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis
Xiaoyu Guo, Jing Ma and Arkaitz Zubiaga - #211 DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis
Pradyumna Gupta, Himanshu Gupta and Aman Sinha - #257 IIITG-ADBU at SemEval-2020 Task 8: A Multimodal Approach to Detect Offensive, Sarcastic and Humorous Memes
Arup Baruah, Kaushik Das, Ferdous Barbhuiya and Kuntal Dey - #282 NLP_UIOWA at SemEval-2020 Task 8: You're Not the Only One Cursed with Knowledge - Multi Branch Model Memotion Analysis
Ingroj Shrestha and Jonathan Rusert - #206 ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model
Zhengjie Huang, Shikun Feng, Weiyue Su, Xuyi Chen, Shuohuan Wang, Jiaxiang Liu, Xuan Ouyang and Yu Sun - #187 JCT at SemEval-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection
Efrat Amar and Chaya Liebeskind
(NOTE: Task 1 poster presented in this session due to schedule constraints)