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The Forum for Information Retrieval (FIRE) started a shared task this yearfor classification of comments of different code segments. This is binary textclassification task where the objective is to identify whether comments givenfor certain code segments are relevant or not. The BioNLP-IISERB group at theIndian Institute of Science Education and Research Bhopal (IISERB) participatedin this task and submitted five runs for five different models. The paperpresents the overview of the models and other significant findings on thetraining corpus. The methods involve different feature engineering schemes andtext classification techniques. The performance of the classical bag of wordsmodel and transformer-based models were explored to identify significantfeatures from the given training corpus. We have explored different classifiersviz., random forest, support vector machine and logistic regression using thebag of words model. Furthermore, the pre-trained transformer based models likeBERT, RoBERT and ALBERT were also used by fine-tuning them on the giventraining corpus. The performance of different such models over the trainingcorpus were reported and the best five models were implemented on the giventest corpus. The empirical results show that the bag of words model outperformsthe transformer based models, however, the performance of our runs are notreasonably well in both training and test corpus. This paper also addresses thelimitations of the models and scope for further improvement.

Identification of the Relevance of Comments in Codes Using Bag of Words and Transformer Based Models