BpHigh@TamilNLP-ACL2022: Effects of Data Augmentation on Indic-Transformer based classifier for Abusive Comments Detection in Tamil

Kernels used for model training using Kaggle Notebooks and other experimental kernels as well as Kernels used for data augmentation purposes.

Repo-Visits

Background

• The shared task on Abusive comment detection in Tamil-ACL 2022 is a comment classification problem that can be further described as a multi-class text classification problem in Tamil native script and Tamil-English code-mixed.

• Given a YouTube comment, the systems submitted by the participants should classify its abusive categories.

• The participants were provided with development, training and test dataset in Tamil and Tamil-English.

• The dataset is tagged using various classes namely, Homophobia, Misandry, Counter-speech, Misogyny, Xenophobia, Transphobic and hope speech.

• The dataset consists of rows that contain the comment text and the label assigned to that comment

Contributors

• Bhavish Pahwa 🏄‍♂️ (GitHub)

Methodology

• We build a classifier using the MURIL Transformer as our embedding layer(all layers frozen) and attach a classifier head by adding subsequent convolution and dense layers. The final output dense layer has softmax activation, which gives us the final predictions.

• We use two data Augmentation approaches to improve our model performance.

• We define an equation to generate a balanced form of the original shared task dataset through our augmentation approaches.

• We take the help of the NlpAug library , which provides the methods to perform word-level augmentation using contextual models as well as non-contextual word embeddings like Word2vec, fastText, and Glove.

Equation used for deciding the number of samples to augment for each class

• The above equation shows us the multiplier value M, used while generating the augmented sentences. M refers to the value by which the number of occurrences of a label should change, and N is the number of occurrences of a label, also called the value count of a label. L refers to the set of class labels.

• In terms of words, the above equation conveys that the multiplier value M(i) for label i is equal to the floor division of the value count for the label having maximum count and the value count for label i.

Data Augmentation Approaches Used

• We use the MURIL Transformer again as a "Contextual Word Embedding Augmenter" to generate word-level augmented sentences. Then we train our classifier using this new balanced version of the train dataset.

• We use the IndicNLP tokenizer for Indian languages for pre-processing the input sentences and the Tamil fastText model from the IndicNLP suite as a ’Word Embeddings Augmenter’ to generate word-level augmented sentences. Then we train our classifier using this new balanced version of the train dataset.

Results