LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and Voting
Abheesht Sharma, Harshit Pandey, Gunjan Chhablani, Yash Bhartia, Tirtharaj Dash · 2021
In this article, we present our methodologies for SemEval-2021 Task-4: Reading Comprehension of Abstract Meaning.Given a fill-inthe-blank-type question and a corresponding context, the task is to predict the most suitable word from a list of 5 options.There are three sub-tasks within this task: Imperceptibility (subtask-I), Non-Specificity (subtask-II), and Intersection (subtask-III).We use encoders of transformers-based models pre-trained on the masked language modelling (MLM) task to build our Fill-in-the-blank (FitB) models.Moreover, to model imperceptibility, we define certain linguistic features, and to model non-specificity, we leverage information from hypernyms and hyponyms provided by a lexical database.Specifically, for non-specificity, we try out augmentation techniques, and other statistical techniques.We also propose variants, namely Chunk Voting and Max Context, to take care of input length restrictions for BERT, etc.Additionally, we perform a thorough ablation study, and use Integrated Gradients to explain our predictions on a few samples.Our best submissions achieve accuracies of 75.31% and 77.84%, on the test sets for subtask-I and subtask-II, respectively.For subtask-III, we achieve accuracies of 65.64% and 62.27%.The code is available here.