<p>Sarcastic posts are common on social media, where user’s express sentiments that differ from the literal meaning of their words. Accurately understanding the intended meaning behind these posts could greatly enhance the effectiveness of text analysis tools for social media, a topic that remains under-explored. This research introduces a bidirectional long and short term memory (BiLSTM) based encoder-decoder model incorporating an attention mechanism and SpaCy-based embeddings. The model processes sarcastic input text and generates their corresponding non-sarcastic interpretations. A key feature of the proposed model is its bidirectional architecture, enabling it to capture contextual information in both forward and backward directions. By utilizing an attention mechanism, the model focuses on relevant preceding and succeeding words to make accurate predictions. Additionally, external embeddings have been integrated into the model to enhance its performance, addressing limitations in existing sarcasm interpretation models. Which operate unidirectionally without leveraging external embeddings and attention mechanisms. The results demonstrate that the proposed model outperforms traditional unidirectional models in context generation, due to its attention-based bidirectional nature with external embedding for interpreting sarcasm.</p>