Bidirectional RNN/LSTM Tool
Visualize how bidirectional neural networks process sequences in both forward and backward directions to capture context from past and future information simultaneously.
What are Bidirectional Models?
Bidirectional RNNs/LSTMs process sequences in both forward and backward directions, allowing them to capture context from both past and future information. This makes them particularly powerful for tasks like named entity recognition, part-of-speech tagging, and sentiment analysis.
Input Sequence
Bidirectional Model Configuration
Bidirectional Processing Visualization
Processing Bidirectionally...
Analyzing sequence in forward and backward directions
Output
How Bidirectional Processing Works
The forward RNN processes the sequence from left to right, capturing past context. The backward RNN processes the sequence from right to left, capturing future context. Their outputs are combined at each step to create a representation that contains information from both directions.
Analysis & Results
Bidirectional models excel at sequence labeling tasks like POS tagging because they can see both preceding and following context for each word.
Compare the performance of unidirectional (forward-only) models with bidirectional models on different NLP tasks.
Forward-Only RNN
Bidirectional RNN
Performance Improvement
Why Bidirectional Models Perform Better
Many words require future context for correct interpretation. For example, in "bank" (financial vs. river), the surrounding words determine the meaning. Bidirectional models can see both sides of each word, making them superior for contextual understanding tasks.
These are simulated hidden state values for the forward and backward passes, along with their combined representation.
| Word | Forward State | Backward State | Combined | Confidence |
|---|
Understanding Hidden States
Forward State: Encodes information from the beginning of the sequence up to the current word.
Backward State: Encodes information from the end of the sequence back to the current word.
Combined: Concatenation or summation of both states, containing full context.
Confidence: Model's certainty in its prediction for each word.
How to Add This Bidirectional Tool to Your Blogger Site
Step 1: Copy All Code
Select all the code on this page (click and drag or press Ctrl+A then Ctrl+C). The entire page is a single HTML file.
Step 2: Create New Blog Post
In your Blogger dashboard, create a new post or edit an existing one where you want to add the tool.
Step 3: Switch to HTML Mode
Click the "HTML" button in the post editor to switch from Compose to HTML mode.
Step 4: Paste & Publish
Paste the copied code (Ctrl+V) into the HTML editor, then publish or update your post.
Where Are Bidirectional Models Used?
Bidirectional RNNs/LSTMs are foundational to modern NLP systems: BERT (uses bidirectional Transformer), Named Entity Recognition (identifying people, organizations, locations), Part-of-Speech Tagging (grammatical analysis), Sentiment Analysis (understanding emotion in text), Machine Translation (context-aware translation), and Speech Recognition (contextual audio processing).
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