  Online gender-based harassment is a widespread issue limiting the free
expression and participation of women and marginalized genders in digital
spaces. Detecting such abusive content can enable platforms to curb this
menace. We participated in the Gendered Abuse Detection in Indic Languages
shared task at ICON2023 that provided datasets of annotated Twitter posts in
English, Hindi and Tamil for building classifiers to identify gendered abuse.
Our team CNLP-NITS-PP developed an ensemble approach combining CNN and BiLSTM
networks that can effectively model semantic and sequential patterns in textual
data. The CNN captures localized features indicative of abusive language
through its convolution filters applied on embedded input text. To determine
context-based offensiveness, the BiLSTM analyzes this sequence for dependencies
among words and phrases. Multiple variations were trained using FastText and
GloVe word embeddings for each language dataset comprising over 7,600
crowdsourced annotations across labels for explicit abuse, targeted minority
attacks and general offences. The validation scores showed strong performance
across f1-measures, especially for English 0.84. Our experiments reveal how
customizing embeddings and model hyperparameters can improve detection
capability. The proposed architecture ranked 1st in the competition, proving
its ability to handle real-world noisy text with code-switching. This technique
has a promising scope as platforms aim to combat cyber harassment facing Indic
language internet users. Our Code is at
https://github.com/advaithavetagiri/CNLP-NITS-PP
