Abheesht Sharma


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DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature
Abheesht Sharma | Gunjan Chhablani | Harshit Pandey | Rajaswa Patil
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

In this work, we present to the NLP community, and to the wider research community as a whole, an application for the diachronic analysis of research corpora. We open source an easy-to-use tool coined DRIFT, which allows researchers to track research trends and development over the years. The analysis methods are collated from well-cited research works, with a few of our own methods added for good measure. Succinctly put, some of the analysis methods are: keyword extraction, word clouds, predicting declining/stagnant/growing trends using Productivity, tracking bi-grams using Acceleration plots, finding the Semantic Drift of words, tracking trends using similarity, etc. To demonstrate the utility and efficacy of our tool, we perform a case study on the cs.CL corpus of the arXiv repository and draw inferences from the analysis methods. The toolkit and the associated code are available here: https://github.com/rajaswa/DRIFT.

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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
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

We present our approaches and methods for SemEval-2021 Task-4 Reading Comprehension of Abstract Meaning. Given a question with a fill-in-the-blank, and a corresponding context, the task is to predict the most suitable word from a list of 5 options. There are three subtasks: Imperceptibility, Non-Specificity and Intersection. We use encoders of transformers-based models pretrained on the 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 models 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 64.27%.

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NLRG at SemEval-2021 Task 5: Toxic Spans Detection Leveraging BERT-based Token Classification and Span Prediction Techniques
Gunjan Chhablani | Abheesht Sharma | Harshit Pandey | Yash Bhartia | Shan Suthaharan
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

Toxicity detection of text has been a popular NLP task in the recent years. In SemEval-2021 Task-5 Toxic Spans Detection, the focus is on detecting toxic spans within English passages. Most state-of-the-art span detection approaches employ various techniques, each of which can be broadly classified into Token Classification or Span Prediction approaches. In our paper, we explore simple versions of both of these approaches and their performance on the task. Specifically, we use BERT-based models - BERT, RoBERTa, and SpanBERT for both approaches. We also combine these approaches and modify them to bring improvements for Toxic Spans prediction. To this end, we investigate results on four hybrid approaches - Multi-Span, Span+Token, LSTM-CRF, and a combination of predicted offsets using union/intersection. Additionally, we perform a thorough ablative analysis and analyze our observed results. Our best submission - a combination of SpanBERT Span Predictor and RoBERTa Token Classifier predictions - achieves an F1 score of 0.6753 on the test set. Our best post-eval F1 score is 0.6895 on intersection of predicted offsets from top-3 RoBERTa Token Classification checkpoints. These approaches improve the performance by 3% on average than those of the shared baseline models - RNNSL and SpaCy NER.