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

翻译：网络性别骚扰是一个普遍问题，限制了女性和边缘化性别群体在数字空间中的自由表达与参与。检测此类辱骂内容可帮助平台遏制这一威胁。我们参与了ICON2023的印度语言性别歧视性辱骂检测共享任务，该任务提供了英语、印地语和泰米尔语中标注的推特帖子数据集，用于构建分类器以识别性别歧视性辱骂。我们团队CNLP-NITS-PP开发了一种结合CNN和BiLSTM网络的集成方法，能有效建模文本数据中的语义和序列模式。CNN通过应用于嵌入输入文本的卷积滤波器捕获指示辱骂性语言的局部特征，而BiLSTM则分析序列中词语和短语之间的依赖关系以确定基于上下文的冒犯性。我们使用FastText和GloVe词向量针对每个语言数据集训练了多个变体，这些数据集包含超过7600条众包标注，涵盖显性辱骂、针对少数群体的攻击和一般冒犯等标签。验证分数显示其在F1指标上表现强劲，尤其是英语达到0.84。我们的实验揭示了个性化词向量和模型超参数如何提升检测能力。所提出的架构在比赛中排名第一，证明了其处理真实世界含代码切换的噪声文本的能力。随着平台致力于打击针对印度语言互联网用户的网络骚扰，该技术具有广阔的应用前景。我们的代码位于https://github.com/advaithavetagiri/CNLP-NITS-PP