# Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
## 3 Proposed Work
### Classification Using Advanced Models
Given a sequence \(X=\{x_{1},x_{2},\ldots,x_{n}\}\), the hidden state \(h_{t}\) at each time step \(t\) is computed by concatenating the forward and backward states:  
\[\overrightarrow{h_{t}}=\text{LSTM}_{\text{forward}}(x_{t}),\quad \overleftarrow{h_{t}}=\text{LSTM}_{\text{backward}}(x_{t}),\]  
\[h_{t}=[\overrightarrow{h_{t}};\overleftarrow{h_{t}}]\]  
The ability of BiLSTM to capture bidirectional dependencies is particularly advantageous in language models where context from both directions contributes to improved predictions. Another powerful architecture, the **Gated Recurrent Unit (GRU)**, simplifies the LSTM structure by merging the input and forget gates into an update gate. The GRU cell is defined as:  
\[z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}]),\quad r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}]),\]  
\[\tilde{h}_{t}=\tanh(W_{h}\cdot[r_{t}*h_{t-1},x_{t}]),\quad h_{t}=(1-z_{t})*h_{ t-1}+z_{t}*\tilde{h}_{t}\]  
where \(z_{t}\) and \(r_{t}\) are the update and reset gates. The GRU's streamlined architecture provides faster computation, making it suitable for real-time predictive tasks. The **BERT (Bidirectional Encoder Representations from Transformers)** model, based on Transformer architecture, uses masked language modeling (MLM) and next sentence prediction (NSP) for pretraining [38]. The core operation in BERT is the self-attention mechanism:  
\[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}}\bigg{)}V\]  
where \(Q\), \(K\), and \(V\) are the query, key, and value matrices, and \(d_{k}\) represents the dimension of the key vectors. BERT's bidirectional attention mechanism enables it to generate context-aware representations, improving the accuracy of complex NLP predictions. An extension of BERT, **RoBERTa (A Robustly Optimized BERT Pretraining Approach)**, enhances BERT's performance by refining the training process [39]. RoBERTa removes the NSP task, increases batch sizes, and optimizes hyperparameters while retaining the self-attention mechanism. \[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}} \bigg{)}V\]  
These adjustments improve prediction quality in NLP tasks, achieving state-of-the-art performance on many benchmarks. Finally, **DistilRoBERTa** is a distilled version of RoBERTa, achieving similar performance while reducing parameters and computational cost [40]. DistilRoBERTa employs knowledge distillation, where a smaller student model learns from a larger teacher model:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}),\quad i_{t}=\sigma(W_{i}\cdot[h_ {t-1},x_{t}]+b_{i}),\]0  
where \(Z_{s}\) and \(Z_{t}\) are the logits of the student and teacher models, \(T\) is the temperature, and \(L_{\text{CE}}\) is the cross-entropy loss. This process makes DistilRoBERTa efficient for deployment on limited-resource devices, with reliable predictive power. **XLNet**, a model built upon the Transformer-XL architecture, employs an autoregressive approach for pretraining while integrating a permutation-based language modeling objective [41]. This unique mechanism allows XLNet to learn bidirectional context without using masked tokens:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}),\quad i_{t}=\sigma(W_{i}\cdot[h_ {t-1},x_{t}]+b_{i}),\]1  
where \(\pi\) is a permutation of the sequence indices. XLNet's structure enables better handling of long-term dependencies, making it particularly effective for text prediction and NLP tasks that require extensive contextual understanding. **ALBERT (A Lite BERT)** is an efficient variant of BERT that incorporates parameter sharing and factorized embeddings to reduce the model's size without compromising performance [42]. The main mathematical approach includes:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}),\quad i_{t}=\sigma(W_{i}\cdot[h_ {t-1},x_{t}]+b_{i}),\]2  
where \(E_{s}\) is the smaller embedding matrix and \(E_{p}\) is the projection matrix. This approach improves memory efficiency and training speed, making ALBERT highly advantageous for scalable NLP tasks.