Table of Contents

• May I Ask Who's Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law ComplianceMicaela Kaplan. 1-6 [doi]
• "Did you really mean what you said?" : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word EmbeddingsAkshita Aggarwal, Anshul Wadhawan, Anshima Chaudhary, Kavita Maurya. 7-15 [doi]
• Noisy Text Data: Achilles' Heel of BERTAnkit Kumar, Piyush Makhija, Anuj Gupta. 16-21 [doi]
• Determining Question-Answer Plausibility in Crowdsourced Datasets Using Multi-Task LearningRachel Gardner, Maya Varma, Clare Zhu, Ranjay Krishna. 22-27 [doi]
• Combining BERT with Static Word Embeddings for Categorizing Social MediaIsraa Alghanmi, Luis Espinosa Anke, Steven Schockaert. 28-33 [doi]
• Enhanced Sentence Alignment Network for Efficient Short Text MatchingZhe Hu, Zuohui Fu, Cheng Peng, Weiwei Wang. 34-40 [doi]
• PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine TranslationVivek Srivastava, Mayank Singh 0001. 41-49 [doi]
• Cross-lingual sentiment classification in low-resource Bengali languageSalim Sazzed. 50-60 [doi]
• The Non-native Speaker Aspect: Indian English in Social MediaRupak Sarkar, Sayantan Mahinder, Ashiqur R. KhudaBukhsh. 61-70 [doi]
• Sentence Boundary Detection on Line Breaks in JapaneseYuta Hayashibe, Kensuke Mitsuzawa. 71-75 [doi]
• Non-ingredient Detection in User-generated Recipes using the Sequence Tagging ApproachYasuhiro Yamaguchi, Shintaro Inuzuka, Makoto Hiramatsu, Jun Harashima. 76-80 [doi]
• Generating Fact Checking Summaries for Web ClaimsRahul Mishra, Dhruv Gupta, Markus Leippold. 81-90 [doi]
• Intelligent Analyses on Storytelling for Impact MeasurementKoen Kicken, Tessa De Maesschalck, Bart Vanrumste, Tom De Keyser, Heereen Shim. 91-100 [doi]
• An Empirical Analysis of Human-Bot Interaction on RedditMing-Cheng Ma, John P. Lalor. 101-106 [doi]
• Detecting Trending Terms in Cybersecurity Forum DiscussionsJack Hughes, Seth Aycock, Andrew Caines, Paula Buttery, Alice Hutchings. 107-115 [doi]
• Service registration chatbot: collecting and comparing dialogues from AMT workers and service's usersLuca Molteni, Mittul Singh, Juho Leinonen, Katri Leino, Mikko Kurimo, Emanuele Della Valle. 116-121 [doi]
• Automated Assessment of Noisy Crowdsourced Free-text Answers for Hindi in Low Resource SettingDolly Agarwal, Somya Gupta, Nishant Baghel. 122-131 [doi]
• Punctuation Restoration using Transformer Models for High-and Low-Resource LanguagesTanvirul Alam, Akib Khan, Firoj Alam. 132-142 [doi]
• Truecasing German user-generated conversational textYulia Grishina, Thomas Gueudre, Ralf Winkler. 143-148 [doi]
• Fine-Tuning MT systems for Robustness to Second-Language Speaker VariationsMd Mahfuz Ibn Alam, Antonios Anastasopoulos. 149-158 [doi]
• Impact of ASR on Alzheimer's Disease Detection: All Errors are Equal, but Deletions are More Equal than OthersAparna Balagopalan, Ksenia Shkaruta, Jekaterina Novikova. 159-164 [doi]
• Detecting Entailment in Code-Mixed Hindi-English ConversationsSharanya Chakravarthy, Anjana Umapathy, Alan W. Black. 165-170 [doi]
• Detecting Objectifying Language in Online Professor ReviewsAngie Waller, Kyle Gorman. 171-180 [doi]
• Annotation Efficient Language Identification from Weak LabelsShriphani Palakodety, Ashiqur R. KhudaBukhsh. 181-192 [doi]
• Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided ApproachBenjamin Eyre, Aparna Balagopalan, Jekaterina Novikova. 193-199 [doi]
• Quantifying the Evaluation of Heuristic Methods for Textual Data AugmentationOmid Kashefi, Rebecca Hwa. 200-208 [doi]
• An Empirical Survey of Unsupervised Text Representation Methods on Twitter DataLili Wang, Chongyang Gao, Jason Wei, Weicheng Ma, Ruibo Liu, Soroush Vosoughi. 209-214 [doi]
• Civil Unrest on Twitter (CUT): A Dataset of Tweets to Support Research on Civil UnrestJustin Sech, Alexandra DeLucia, Anna L. Buczak, Mark Dredze. 215-221 [doi]
• Tweeki: Linking Named Entities on Twitter to a Knowledge GraphBahareh Harandizadeh, Sameer Singh. 222-231 [doi]
• Representation learning of writing styleJulien Hay, Bich-Liên Doan, Fabrice Popineau, Ouassim Ait Elhara. 232-243 [doi]
• "A Little Birdie Told Me ... " - Inductive Biases for Rumour Stance Detection on Social MediaKarthik Radhakrishnan, Tushar Kanakagiri, Sharanya Chakravarthy, Vidhisha Balachandran. 244-248 [doi]
• Paraphrase Generation via Adversarial PenalizationsGerson Vizcarra, José Ochoa Luna. 249-259 [doi]
• WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab ProtocolsJeniya Tabassum, Wei Xu 0004, Alan Ritter. 260-267 [doi]
• IITKGP at W-NUT 2020 Shared Task-1: Domain specific BERT representation for Named Entity Recognition of lab protocolTejas Vaidhya, Ayush Kaushal. 268-272 [doi]
• PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised EmbeddingsJanvijay Singh, Anshul Wadhawan. 273-280 [doi]
• Big Green at WNUT 2020 Shared Task-1: Relation Extraction as Contextualized Sequence ClassificationChris Miller, Soroush Vosoughi. 281-285 [doi]
• WNUT 2020 Shared Task-1: Conditional Random Field(CRF) based Named Entity Recognition(NER) for Wet Lab ProtocolsKaushik Acharya. 286-289 [doi]
• mgsohrab at WNUT 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab ProtocolsMohammad Golam Sohrab, Anh-Khoa Duong Nguyen, Makoto Miwa, Hiroya Takamura. 290-298 [doi]
• Fancy Man Launches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity ExtractionQingcheng Zeng, Xiaoyang Fang, Zhexin Liang, Haoding Meng. 299-304 [doi]
• BiTeM at WNUT 2020 Shared Task-1: Named Entity Recognition over Wet Lab Protocols using an Ensemble of Contextual Language ModelsJulien Knafou, Nona Naderi, Jenny Copara, Douglas Teodoro, Patrick Ruch. 305-313 [doi]
• WNUT-2020 Task 2: Identification of Informative COVID-19 English TweetsDat Quoc Nguyen, Thanh Vu, Afshin Rahimi, Mai Hoang Dao, Linh The Nguyen, Long Doan. 314-318 [doi]
• TATL at WNUT-2020 Task 2: A Transformer-based Baseline System for Identification of Informative COVID-19 English TweetsAnh Tuan Nguyen. 319-323 [doi]
• NHK_STRL at WNUT-2020 Task 2: GATs with Syntactic Dependencies as Edges and CTC-based Loss for Text ClassificationYuki Yasuda, Taichi Ishiwatari, Taro Miyazaki, Jun Goto. 324-330 [doi]
• NLP North at WNUT-2020 Task 2: Pre-training versus Ensembling for Detection of Informative COVID-19 English TweetsAnders Giovanni Møller, Rob van der Goot, Barbara Plank. 331-336 [doi]
• Siva at WNUT-2020 Task 2: Fine-tuning Transformer Neural Networks for Identification of Informative Covid-19 TweetsSiva Sai. 337-341 [doi]
• IIITBH at WNUT-2020 Task 2: Exploiting the best of both worldsSaichethan Reddy, Pradeep Biswal. 342-346 [doi]
• Phonemer at WNUT-2020 Task 2: Sequence Classification Using COVID Twitter BERT and Bagging Ensemble Technique based on Plurality VotingAnshul Wadhawan. 347-351 [doi]
• CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets - RoBERTa Ensembles and The Continued Relevance of Handcrafted FeaturesCalum Perrio, Harish Tayyar Madabushi. 352-358 [doi]
• InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet ExtractionHansi Hettiarachchi, Tharindu Ranasinghe. 359-365 [doi]
• BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning ModelsTin Van Huynh, Luan Thanh Luan, Son T. Luu. 366-370 [doi]
• DATAMAFIA at WNUT-2020 Task 2: A Study of Pre-trained Language Models along with Regularization Techniques for Downstream TasksAyan Sengupta. 371-377 [doi]
• UPennHLP at WNUT-2020 Task 2 : Transformer models for classification of COVID19 posts on TwitterArjun Magge, Varad Pimpalkhute, Divya Rallapalli, David Siguenza, Graciela Gonzalez-Hernandez. 378-382 [doi]
• UIT-HSE at WNUT-2020 Task 2: Exploiting CT-BERT for Identifying COVID-19 Information on the Twitter Social NetworkKhiem Vinh Tran, Hao Phu Phan, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen. 383-387 [doi]
• Emory at WNUT-2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet IdentificationYuting Guo, Mohammed Ali Al-garadi, Abeed Sarker. 388-393 [doi]
• CSECU-DSG at WNUT-2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative COVID-19 English TweetsFareen Tasneem, Jannatun Naim, Radiathun Tasnia, Tashin Hossain, Abu Nowshed Chy. 394-398 [doi]
• IRLab@IITBHU at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets using BERTSupriya Chanda, Eshita Nandy, Sukomal Pal. 399-403 [doi]
• NutCracker at WNUT-2020 Task 2: Robustly Identifying Informative COVID-19 Tweets using Ensembling and Adversarial TrainingPriyanshu Kumar, Aadarsh Singh. 404-408 [doi]
• DSC-IIT ISM at WNUT-2020 Task 2: Detection of COVID-19 informative tweets using RoBERTaSirigireddy Dhanalaxmi, Rohit Agarwal, Aman Sinha. 409-413 [doi]
• Linguist Geeks on WNUT-2020 Task 2: COVID-19 Informative Tweet Identification using Progressive Trained Language Models and Data AugmentationVasudev Awatramani, Anupam Kumar. 414-418 [doi]
• NLPRL at WNUT-2020 Task 2: ELMo-based System for Identification of COVID-19 TweetsRajesh Kumar Mundotiya, Rupjyoti Baruah, Bhavana Srivastava, Anil Kumar Singh. 419-422 [doi]
• SU-NLP at WNUT-2020 Task 2: The Ensemble ModelsKenan Fayoumi, Reyyan Yeniterzi. 423-427 [doi]
• IDSOU at WNUT-2020 Task 2: Identification of Informative COVID-19 English TweetsSora Ohashi, Tomoyuki Kajiwara, Chenhui Chu, Noriko Takemura, Yuta Nakashima, Hajime Nagahara. 428-433 [doi]
• ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked DocumentsKellin Pelrine, Jacob Danovitch, Albert Orozco Camacho, Reihaneh Rabbany. 434-439 [doi]
• NEU at WNUT-2020 Task 2: Data Augmentation To Tell BERT That Death Is Not Necessarily InformativeKumud Chauhan. 440-443 [doi]
• LynyrdSkynyrd at WNUT-2020 Task 2: Semi-Supervised Learning for Identification of Informative COVID-19 English TweetsAbhilasha Sancheti, Kushal Chawla, Gaurav Verma. 444-449 [doi]
• NIT_COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English TweetsJagadeesh M. S, Alphonse P. J. A. 450-454 [doi]
• EdinburghNLP at WNUT-2020 Task 2: Leveraging Transformers with Generalized Augmentation for Identifying Informativeness in COVID-19 TweetsNickil Maveli. 455-461 [doi]
• #GCDH at WNUT-2020 Task 2: BERT-Based Models for the Detection of Informativeness in English COVID-19 Related TweetsHanna Varachkina, Stefan Ziehe, Tillmann Dönicke, Franziska Pannach. 462-465 [doi]
• Not-NUTs at WNUT-2020 Task 2: A BERT-based System in Identifying Informative COVID-19 English TweetsThai Quoc Hoang, Phuong Thu Vu. 466-470 [doi]
• CIA_NITT at WNUT-2020 Task 2: Classification of COVID-19 Tweets Using Pre-trained Language ModelsYandrapati Prakash Babu, Rajagopal Eswari. 471-474 [doi]
• UET at WNUT-2020 Task 2: A Study of Combining Transfer Learning Methods for Text Classification with RoBERTaHuy Dao Quang, Tam Nguyen Minh. 475-479 [doi]
• Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet Classification Using BERTDylan Whang, Soroush Vosoughi. 480-484 [doi]
• SunBear at WNUT-2020 Task 2: Improving BERT-Based Noisy Text Classification with Knowledge of the Data domainLinh Doan Bao, Viet Anh Nguyen, Quang Pham Huu. 485-490 [doi]
• ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText EmbeddingsWava Carissa Putri, Rani Aulia Hidayat, Isnaini Nurul Khasanah, Rahmad Mahendra. 491-494 [doi]
• COVCOR20 at WNUT-2020 Task 2: An Attempt to Combine Deep Learning and Expert rulesAli Hürriyetoglu, Ali Safaya, Osman Mutlu, Nelleke Oostdijk, Erdem Yörük. 495-498 [doi]
• TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modelingChacha Chen, Chieh-Yang Huang, Yaqi Hou, Yang Shi, Enyan Dai, Jiaqi Wang. 499-504 [doi]
• imec-ETRO-VUB at W-NUT 2020 Shared Task-3: A multilabel BERT-based system for predicting COVID-19 eventsXiangyu Yang, Giannis Bekoulis, Nikos Deligiannis. 505-513 [doi]
• UCD-CS at W-NUT 2020 Shared Task-3: A Text to Text Approach for COVID-19 Event Extraction on Social MediaCongcong Wang, David Lillis. 514-521 [doi]
• Winners at W-NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from TweetsAyush Kaushal, Tejas Vaidhya. 522-529 [doi]
• HLTRI at W-NUT 2020 Shared Task-3: COVID-19 Event Extraction from Twitter Using Multi-Task Hopfield PoolingMaxwell A. Weinzierl, Sanda M. Harabagiu. 530-538 [doi]