SU - NLP at WNUT -2020 Task 2: The Ensemble Models (2020)

Abstract: 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.

## introduction after the recent virus outbreak, social media platforms, like twitter, have been flooded with covid-19 related content. some of those content contain useful and valuable information such as the current status of the outbreak in particular locations. however, like any other topic being discussed in social media, the majority of the content is unrelated, subjective, or uninformative. finding informative content would require extensive manual search, which is not scalable due to the size of the content. therefore, automatically identifying both relevant and informative content has become an important task. in this paper, we study this specific problem as part of the w-nut 2020 shared-task on identification of informative covid-19 english tweets, and report our findings. we specifically focused on transformer based neural network architectures and also simple but still effective cnn models. we also experiment with ensembling different models as we attempt to reduce the performance variance introduced from a model and combine strength of models in order to improve the final classification performance. code to our classification and ensemble methods are available at our github repository 1 . ## classification models we approached the problem by first training different classification models, and then combining their strengths. as our first model, we chose a simple yet effective cnn text classification model. as for the rest, three pre-trained transformer-based models were adapted for our particular task. in this section, we discuss these models and their performances. ## ensemble methods in this section we describe our two approaches to address the issue of variance in model performances and improve the overall classification accuracy. our approaches focus on ensembling the probabilities of multiple classification models while making predictions. ## conclusion in this paper, we presented our work for w-nut 2020 task 2: identification of informative covid-19 english tweets. we experimented with bertbased models and cnns as our classification models. after analyzing the classification performance of these different models, we decided to use two different ensembling methods. both of these ensemble strategies outperformed the individual models. more specifically, the ensemble strategy which addressed the variance in model performance caused by the random initialization, returned the best performance.