pwc-archive/papers-with-abstracts huggingface dataset:
Siva at WNUT-2020 Task 2: Fine-tuning Transformer Neural Networks for Identification of Informative Covid-19 Tweets
Authors: Siva Sai
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Misinformation, Task 2

Social media witnessed vast amounts of misinformation being circulated every day during the Covid-19 pandemic so much so that the WHO Director-General termed the phenomenon as “infodemic.” The ill-effects of such misinformation are multifarious. Thus, identifying and eliminating the sources of misinformation becomes very crucial, especially when mass panic can be controlled only through the right information. However, manual identification is arduous, with such large amounts of data being generated every day. This shows the importance of automatic identification of misinformative posts on social media. WNUT-2020 Task 2 aims at building systems for automatic identification of informative tweets. In this paper, I discuss my approach to WNUT-2020 Task 2. I fine-tuned eleven variants of four transformer networks -BERT, RoBERTa, XLM-RoBERTa, ELECTRA, on top of two different preprocessing techniques to reap good results. My top submission achieved an F1-score of 85.3% in the final evaluation.
IIITBH at WNUT-2020 Task 2: Exploiting the best of both worlds
Authors: Saichethan Reddy, Pradeep Biswal
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Task 2

In this paper, we present IIITBH team’s effort to solve the second shared task of the 6th Workshop on Noisy User-generated Text (W-NUT)i.e Identification of informative COVID-19 English Tweets. The central theme of the task is to develop a system that automatically identify whether an English Tweet related to the novel coronavirus (COVID-19) is Informative or not. Our approach is based on exploiting semantic information from both max pooling and average pooling, to this end we propose two models.
CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets - RoBERTa Ensembles and The Continued Relevance of Handcrafted Features
Authors: Calum Perrio, Harish Tayyar Madabushi
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Classification, Language Modeling, Language Modelling, Task 2, text-classification, Text Classification

This paper presents our submission to Task 2 of the Workshop on Noisy User-generated Text. We explore improving the performance of a pre-trained transformer-based language model fine-tuned for text classification through an ensemble implementation that makes use of corpus level information and a handcrafted feature. We test the effectiveness of including the aforementioned features in accommodating the challenges of a noisy data set centred on a specific subject outside the remit of the pre-training data. We show that inclusion of additional features can improve classification results and achieve a score within 2 points of the top performing team.
DATAMAFIA at WNUT-2020 Task 2: A Study of Pre-trained Language Models along with Regularization Techniques for Downstream Tasks
Authors: Ayan Sengupta
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Binary Classification, Task 2

This document describes the system description developed by team datamafia at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets. This paper contains a thorough study of pre-trained language models on downstream binary classification task over noisy user generated Twitter data. The solution submitted to final test leaderboard is a fine tuned RoBERTa model which achieves F1 score of 90.8% and 89.4% on the dev and test data respectively. In the later part, we explore several techniques for injecting regularization explicitly into language models to generalize predictions over noisy data. Our experiments show that adding regularizations to RoBERTa pre-trained model can be very robust to data and annotation noises and can improve overall performance by more than 1.2%.
UPennHLP at WNUT-2020 Task 2 : Transformer models for classification of COVID19 posts on Twitter
Authors: Arjun Magge, Varad Pimpalkhute, Divya Rallapalli, David Siguenza, Graciela Gonzalez-Hernandez
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Task 2

Increasing usage of social media presents new non-traditional avenues for monitoring disease outbreaks, virus transmissions and disease progressions through user posts describing test results or disease symptoms. However, the discussions on the topic of infectious diseases that are informative in nature also span various topics such as news, politics and humor which makes the data mining challenging. We present a system to identify tweets about the COVID19 disease outbreak that are deemed to be informative on Twitter for use in downstream applications. The system scored a F1-score of 0.8941, Precision of 0.9028, Recall of 0.8856 and Accuracy of 0.9010. In the shared task organized as part of the 6th Workshop of Noisy User-generated Text (WNUT), the system was ranked 18th by F1-score and 13th by Accuracy.
UIT-HSE at WNUT-2020 Task 2: Exploiting CT-BERT for Identifying COVID-19 Information on the Twitter Social Network
Authors: Khiem Tran, Hao Phan, Kiet Nguyen, Ngan Luu Thuy Nguyen
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Task 2

Recently, COVID-19 has affected a variety of real-life aspects of the world and led to dreadful consequences. More and more tweets about COVID-19 has been shared publicly on Twitter. However, the plurality of those Tweets are uninformative, which is challenging to build automatic systems to detect the informative ones for useful AI applications. In this paper, we present our results at the W-NUT 2020 Shared Task 2: Identification of Informative COVID-19 English Tweets. In particular, we propose our simple but effective approach using the transformer-based models based on COVID-Twitter-BERT (CT-BERT) with different fine-tuning techniques. As a result, we achieve the F1-Score of 90.94% with the third place on the leaderboard of this task which attracted 56 submitted teams in total.
Emory at WNUT-2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification
Authors: Yuting Guo, Mohammed Ali Al-Garadi, Abeed Sarker
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Task 2

This paper describes the system developed by the Emory team for the WNUT-2020 Task 2: “Identifi- cation of Informative COVID-19 English Tweet”. Our system explores three recent Transformer- based deep learning models pretrained on large- scale data to encode documents. Moreover, we developed two feature enrichment methods to en- hance document embeddings by integrating emoji embeddings and syntactic features into deep learn- ing models. Our system achieved F1-score of 0.897 and accuracy of 90.1% on the test set, and ranked in the top-third of all 55 teams.
CSECU-DSG at WNUT-2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative COVID-19 English Tweets
Authors: Fareen Tasneem, Jannatun Naim, Radiathun Tasnia, Tashin Hossain, Abu Nowshed Chy
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Decision Making, Sentence, Sentence Embedding, Sentence-Embedding, Task 2, Transfer Learning

COVID-19 pandemic has become the trending topic on twitter and people are interested in sharing diverse information ranging from new cases, healthcare guidelines, medicine, and vaccine news. Such information assists the people to be updated about the situation as well as beneficial for public safety personnel for decision making. However, the informal nature of twitter makes it challenging to refine the informative tweets from the huge tweet streams. To address these challenges WNUT-2020 introduced a shared task focusing on COVID-19 related informative tweet identification. In this paper, we describe our participation in this task. We propose a neural model that adopts the strength of transfer learning and hand-crafted features in a unified architecture. To extract the transfer learning features, we utilize the state-of-the-art pre-trained sentence embedding model BERT, RoBERTa, and InferSent, whereas various twitter characteristics are exploited to extract the hand-crafted features. Next, various feature combinations are utilized to train a set of multilayer perceptron (MLP) as the base-classifier. Finally, a majority voting based fusion approach is employed to determine the informative tweets. Our approach achieved competitive performance and outperformed the baseline by 7% (approx.).
IRLab@IITBHU at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets using BERT
Authors: Supriya Chanda, Eshita Nandy, Sukomal Pal
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Task 2

This paper reports our submission to the shared Task 2: Identification of informative COVID-19 English tweets at W-NUT 2020. We attempted a few techniques, and we briefly explain here two models that showed promising results in tweet classification tasks: DistilBERT and FastText. DistilBERT achieves a F1 score of 0.7508 on the test set, which is the best of our submissions.
DSC-IIT ISM at WNUT-2020 Task 2: Detection of COVID-19 informative tweets using RoBERTa
Authors: Sirigireddy Dhana Laxmi, Rohit Agarwal, Aman Sinha
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Task 2

Social media such as Twitter is a hotspot of user-generated information. In this ongoing Covid-19 pandemic, there has been an abundance of data on social media which can be classified as informative and uninformative content. In this paper, we present our work to detect informative Covid-19 English tweets using RoBERTa model as a part of the W-NUT workshop 2020. We show the efficacy of our model on a public dataset with an F1-score of 0.89 on the validation dataset and 0.87 on the leaderboard.
Linguist Geeks on WNUT-2020 Task 2: COVID-19 Informative Tweet Identification using Progressive Trained Language Models and Data Augmentation
Authors: Vasudev Awatramani, Anupam Kumar
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Articles, Data Augmentation, Task 2, Transfer Learning

Since the outbreak of COVID-19, there has been a surge of digital content on social media. The content ranges from news articles, academic reports, tweets, videos, and even memes. Among such an overabundance of data, it is crucial to distinguish which information is actually informative or merely sensational, redundant or false. This work focuses on developing such a language system that can differentiate between Informative or Uninformative tweets associated with COVID-19 for WNUT-2020 Shared Task 2. For this purpose, we employ deep transfer learning models such as BERT along other techniques such as Noisy Data Augmentation and Progress Training. The approach achieves a competitive F1-score of 0.8715 on the final testing dataset.
NLPRL at WNUT-2020 Task 2: ELMo-based System for Identification of COVID-19 Tweets
Authors: Rajesh Kumar Mundotiya, Rupjyoti Baruah, Bhavana Srivastava, Anil Kumar Singh
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Binary Classification, Management, Task 2

The Coronavirus pandemic has been a dominating news on social media for the last many months. Efforts are being made to reduce its spread and reduce the casualties as well as new infections. For this purpose, the information about the infected people and their related symptoms, as available on social media, such as Twitter, can help in prevention and taking precautions. This is an example of using noisy text processing for disaster management. This paper discusses the NLPRL results in Shared Task-2 of WNUT-2020 workshop. We have considered this problem as a binary classification problem and have used a pre-trained ELMo embedding with GRU units. This approach helps classify the tweets with accuracy as 80.85% and 78.54% as F1-score on the provided test dataset. The experimental code is available online.
SU-NLP at WNUT-2020 Task 2: The Ensemble Models
Authors: Kenan Fayoumi, Reyyan Yeniterzi
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Binary Classification, Classification, Task 2

In this paper, we address the problem of identifying informative tweets related to COVID-19 in the form of a binary classification task as part of our submission for W-NUT 2020 Task 2. Specifically, we focus on ensembling methods to boost the classification performance of classification models such as BERT and CNN. We show that ensembling can reduce the variance in performance, specifically for BERT base models.
IDSOU at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
Authors: Sora Ohashi, Tomoyuki Kajiwara, Chenhui Chu, Noriko Takemura, Yuta Nakashima, Hajime Nagahara
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Task 2

We introduce the IDSOU submission for the WNUT-2020 task 2: identification of informative COVID-19 English Tweets. Our system is an ensemble of pre-trained language models such as BERT. We ranked 16th in the F1 score.
ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents
Authors: Kellin Pelrine, Jacob Danovitch, Albert Orozco Camacho, Reihaneh Rabbany
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Graph Classification, Language Modeling, Language Modelling, Task 2

Given the global scale of COVID-19 and the flood of social media content related to it, how can we find informative discussions? We present Gapformer, which effectively classifies content as informative or not. It reformulates the problem as graph classification, drawing on not only the tweet but connected webpages and entities. We leverage a pre-trained language model as well as the connections between nodes to learn a pooled representation for each document network. We show it outperforms several competitive baselines and present ablation studies supporting the benefit of the linked information. Code is available on Github.
NIT_COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English Tweets
Authors: Jagadeesh M S, Alphonse P J A
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Task 2

This paper presents the model submitted by NIT COVID-19 team for identified informative COVID-19 English tweets at WNUT-2020 Task2. This shared task addresses the problem of automatically identifying whether an English tweet related to informative (novel coronavirus) or not. These informative tweets provide information about recovered, confirmed, suspected, and death cases as well as location or travel history of the cases. The proposed approach includes pre-processing techniques and pre-trained RoBERTa with suitable hyperparameters for English coronavirus tweet classification. The performance achieved by the proposed model for shared task WNUT 2020 Task2 is 89.14% in the F1-score metric.
#GCDH at WNUT-2020 Task 2: BERT-Based Models for the Detection of Informativeness in English COVID-19 Related Tweets
Authors: Hanna Varachkina, Stefan Ziehe, Tillmann Dönicke, Franziska Pannach
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Informativeness, Task 2

In this system paper, we present a transformer-based approach to the detection of informativeness in English tweets on the topic of the current COVID-19 pandemic. Our models distinguish informative tweets, i.e. tweets containing statistics on recovery, suspected and confirmed cases and COVID-19 related deaths, from uninformative tweets. We present two transformer-based approaches as well as a Naive Bayes classifier and a support vector machine as baseline systems. The transformer models outperform the baselines by more than 0.1 in F1-score, with F1-scores of 0.9091 and 0.9036. Our models were submitted to the shared task Identification of informative COVID-19 English tweets WNUT-2020 Task 2.
Not-NUTs at WNUT-2020 Task 2: A BERT-based System in Identifying Informative COVID-19 English Tweets
Authors: Thai Hoang, Phuong Vu
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Task 2

As of 2020 when the COVID-19 pandemic is full-blown on a global scale, people’s need to have access to legitimate information regarding COVID-19 is more urgent than ever, especially via online media where the abundance of irrelevant information overshadows the more informative ones. In response to such, we proposed a model that, given an English tweet, automatically identifies whether that tweet bears informative content regarding COVID-19 or not. By ensembling different BERTweet model configurations, we have achieved competitive results that are only shy of those by top performing teams by roughly 1% in terms of F1 score on the informative class. In the post-competition period, we have also experimented with various other approaches that potentially boost generalization to a new dataset.
UET at WNUT-2020 Task 2: A Study of Combining Transfer Learning Methods for Text Classification with RoBERTa
Authors: Huy Dao Quang, Tam Nguyen Minh
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Task 2, text-classification, Text Classification, Transfer Learning

This paper reports our approach and the results of our experiments for W-NUT task 2: Identification of Informative COVID-19 English Tweets. In this paper, we test out the effectiveness of transfer learning method with state of the art language models as RoBERTa on this text classification task. Moreover, we examine the benefit of applying additional fine-tuning and training techniques including fine-tuning discrimination, gradual unfreezing as well as our custom head for the classifier. Our best model results in a high F1-score of 89.89 on the task’s private test dataset and that of 90.96 on public test set without ensembling multiple models and additional data.
SunBear at WNUT-2020 Task 2: Improving BERT-Based Noisy Text Classification with Knowledge of the Data domain
Authors: Linh Doan Bao, Viet Anh Nguyen, Quang Pham Huu
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Language Modeling, Language Modelling, Task 2, text-classification, Text Classification

This paper proposes an improved custom model for WNUT task 2: Identification of Informative COVID-19 English Tweet. We improve experiment with the effectiveness of fine-tuning methodologies for state-of-the-art language model RoBERTa. We make a preliminary instantiation of this formal model for the text classification approaches. With appropriate training techniques, our model is able to achieve 0.9218 F1-score on public validation set and the ensemble version settles at top 9 F1-score (0.9005) and top 2 Recall (0.9301) on private test set.
ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings
Authors: Wava Carissa Putri, Rani Aulia Hidayat, Isnaini Nurul Khasanah, Rahmad Mahendra
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Task 2, Word Embeddings

This paper presents Iswara’s participation in the WNUT-2020 Task 2 “Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings”,which tries to classify whether a certain tweet is considered informative or not. We proposed a method that utilizes word embeddings and using word occurrence related to the topic for this task. We compare several models to get the best performance. Results show that pairing BERT with word occurrences outperforms fastText with F1-Score, precision, recall, and accuracy on test data of 76%, 81%, 72%, and 79%, respectively
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling
Authors: Chacha Chen, Chieh-Yang Huang, Yaqi Hou, Yang Shi, Enyan Dai, Jiaqi Wang
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Extracting COVID-19 Events from Twitter, Language Modeling, Language Modelling, Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets. The built system should identify different pre-defined slots for each event, in order to answer important questions (e.g., Who is tested positive? What is the age of the person? Where is he/she?). To tackle these challenges, we propose the Joint Event Multi-task Learning (JOELIN) model. Through a unified global learning framework, we make use of all the training data across different events to learn and fine-tune the language model. Moreover, we implement a type-aware post-processing procedure using named entity recognition (NER) to further filter the predictions. JOELIN outperforms the BERT baseline by 17.2% in micro F1.
imec-ETRO-VUB at W-NUT 2020 Shared Task-3: A multilabel BERT-based system for predicting COVID-19 events
Authors: Xiangyu Yang, Giannis Bekoulis, Nikos Deligiannis
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Event Extraction, Language Modeling, Language Modelling

In this paper, we present our system designed to address the W-NUT 2020 shared task for COVID-19 Event Extraction from Twitter. To mitigate the noisy nature of the Twitter stream, our system makes use of the COVID-Twitter-BERT (CT-BERT), which is a language model pre-trained on a large corpus of COVID-19 related Twitter messages. Our system is trained on the COVID-19 Twitter Event Corpus and is able to identify relevant text spans that answer pre-defined questions (i.e., slot types) for five COVID-19 related events (i.e., TESTED POSITIVE, TESTED NEGATIVE, CAN-NOT-TEST, DEATH and CURE & PREVENTION). We have experimented with different architectures; our best performing model relies on a multilabel classifier on top of the CT-BERT model that jointly trains all the slot types for a single event. Our experimental results indicate that our Multilabel-CT-BERT system outperforms the baseline methods by 7 percentage points in terms of micro average F1 score. Our model ranked as 4th in the shared task leaderboard.
Winners at W-NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets
Authors: Ayush Kaushal, Tejas Vaidhya
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Sentence, Sentence Classification, slot-filling, Slot Filling

Twitter has acted as an important source of information during disasters and pandemic, especially during the times of COVID-19. In this paper, we describe our system entry for WNUT 2020 Shared Task-3. The task was aimed at automating the extraction of a variety of COVID-19 related events from Twitter, such as individuals who recently contracted the virus, someone with symptoms who were denied testing and believed remedies against the infection. The system consists of separate multi-task models for slot-filling subtasks and sentence-classification subtasks, while leveraging the useful sentence-level information for the corresponding event. The system uses COVID-Twitter-BERT with attention-weighted pooling of candidate slot-chunk features to capture the useful information chunks. The system ranks 1st at the leaderboard with F1 of 0.6598, without using any ensembles or additional datasets.