IIITBH at WNUT -2020 Task 2: Exploiting the best of both worlds (2020)

Abstract: 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.

## introduction covid-19 pandemic started in wuhan, china in december 2019, caused by the infection of individuals by the severe acute respiratory syndrome coronavirus 2 (sars-cov-2) this dangerous virus is spreading around the world since then. the covid-19 pandemic continues to have a devastating effect on the health and well-being of the global population. it is creating fear and panic for people all around the world, while the vaccine can hopefully brings the situation under control soon. to track the development of the outbreak and to provide users with the information related to the virus, e.g. any new cases in the user's regions. need for building real-time monitoring system which uses social network data like twitter is high. however, manual approaches to identify the informative tweets require significant human efforts and thus are costly. to help handle this problem, wnut shared task 2 @xcite aim participants to build systems to automatically identify whether a covid-19 english tweet is informative or not. such informative tweets provide information about recovered, suspected, confirmed and death cases as well as location or travel history of the cases. ## proposed methodology in our proposed architecture, we aim to leverage the semantic information from both pooling layers for identifying whether given tweet is informative or not. in this section, we describe our method (base model illustrated in figure 2 ) and elaborate on each part with details. ## experimental setup our model is implemented in tensorflow foot_0 and keras foot_1 . we use a batch size of @xmath0, we train our neural network for 25 epochs with the adam optimizer. a dropout and recurrent dropout of 0.25 is used. complete code is made available on github foot_2 . during the pre processing stage of data we removed all unwanted symbols and user mentions. largeuncased bert model is employed for obtaining tweet embeddings. we also analysed how accuracy and loss of max pooling and average pooling changes with number of epochs in different contextual embeddings @xcite @xcite complete code and plots are uploaded in our repository. ## conclusion in this paper we presented our system to wnut 2020 shared task on "identification of informative covid-19 english tweets". traditional text classification models mainly focus on three topics: feature engineering, feature selection and using different types of machine learning algorithms. our main goal in this paper is to show how we can leverages on different pooling methods of bilstm, without using any human-engineered features and improve efficacy of any system. we believe performance of our system can be further improved by tweaking hyper parameters. in future we would like to explore how our models perform with different attention mechanisms @xcite for different tasks like relation classification @xcite , image captioning @xcite , and machine translation @xcite .