The model doesn’t remember previous state across batches since we feed 0s as previous state at start of batch. It’s like asking 2 seperate questions:
- Is there a trigger word in [d1, d2,d3,d4,d5] ?
- Is there a trigger word in [d3,d4,d5,d6,d7] ?
Bi-directional RNNs (BRNN) process inputs from both ends. Please read the following replies:
- Bidirectional layer for time series forecasting - #4 by balaji.ambresh
- Difference between BRNN vs GRU as far as scenario output is concerned - #2 by balaji.ambresh
Hope it’s now clear why it’s important to make the entire input sequence available for making a prediction. All unidirectional RNNs emit output with context from only one direction. This helps detect trigger word as soon as it’s said. BRNN is likely to outperform unidirectional RNNs when it comes to language translation task where reference to words on either sides of the current word are important to emit a good output. Do cover the sections on transformer to get a better understanding of this concept.