Bidirectional LSTM & Transformers

 

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A Bidirectional LSTM (Long Short-Term Memory) is a type of Recurrent Neural Network (RNN) that processes input sequences in both forward and backward directions. This allows the model to capture both past and future contexts, improving performance on tasks like language modeling, sentiment analysis, and machine translation.

Key aspects:

Two LSTM layers: one processing the input sequence from start to end, and another from end to start

Outputs from both layers are combined to form the final representation

Transformers

Transformers are a type of neural network architecture introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. They're primarily designed for sequence-to-sequence tasks like machine translation, but have since been widely adopted for other NLP tasks.

Key aspects:

Self-Attention mechanism: allows the model to attend to all positions in the input sequence simultaneously

Encoder-Decoder architecture: the encoder processes the input sequence, and the decoder generates the output sequence

Here are some guidelines on when to use Bidirectional LSTMs and Transformers, along with examples and code snippets:

Bidirectional LSTM

Use Bidirectional LSTMs when:

You need to model sequential data with strong temporal dependencies (e.g., speech, text, time series data)

You want to capture both past and future contexts for a specific task (e.g., language modeling, sentiment analysis)

Example:

Sentiment Analysis: Predict the sentiment of a sentence using a Bidirectional LSTM

Python

from keras.layers import Bidirectional, LSTM, Dense

from keras.models import Sequential

model = Sequential()

model.add(Bidirectional(LSTM(64), input_shape=(100, 10)))

model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam')

Transformer

Use Transformers when:

You need to process long-range dependencies in sequences (e.g., machine translation, text summarization)

You want to leverage self-attention mechanisms to model complex relationships between input elements

Example:

Machine Translation: Translate English sentences to Spanish using a Transformer

Python

from transformers import Transformer, EncoderDecoder

from torch.nn import CrossEntropyLoss

model = Transformer(d_model=256, nhead=8, num_encoder_layers=6, num_decoder_layers=6)

criterion = CrossEntropyLoss()

Note: The code snippets are simplified examples and may require additional layers, preprocessing, and fine-tuning for actual tasks.

Key differences

Bidirectional LSTMs are suitable for tasks with strong temporal dependencies, while Transformers excel at modeling long-range dependencies and complex relationships.

Bidirectional LSTMs process sequences sequentially, whereas Transformers process input sequences in parallel using self-attention.

When in doubt, start with a Bidirectional LSTM for tasks with strong temporal dependencies, and consider Transformers for tasks requiring long-range dependency modeling.