Sequence to Sequence (often abbreviated to seq2seq) models is a special class of Recurrent Neural Network architectures that we typically use (but not restricted) to solve complex Language problems like Machine Translation, Question Answering, creating Chatbots, Text Summarization, etc.
In this article, I would give you an overview of sequence to sequence models which became quite popular for different tasks like machine translation, video captioning, image captioning, question answering, etc.
Prerequisites: The reader should already be familiar with neural networks and, in particular, recurrent neural networks (RNNs). In addition, knowledge of LSTM or GRU models is preferable. If you are not familiar with LSTM I would prefer you to read LSTM- Long Short-Term Memory.
Sequence to sequence models lies behind numerous systems that you face on a daily basis. For instance, seq2seq model powers applications like Google Translate, voice-enabled devices, and online chatbots. The following are some of the applications:
These are only some applications where seq2seq is seen as the best solution. This model can be used as a solution to any sequence-based problem, especially ones where the inputs and outputs have different sizes and categories.
We will talk more about the model structure below.
The most common architecture used to build Seq2Seq models is Encoder-Decoder architecture.
As the name implies, there are two components — an encoder and a decoder.
The LSTM reads the data, one sequence after the other. Thus if the input is a sequence of length ‘t’, we say that LSTM reads it in ‘t’ time steps.
1. Xi = Input sequence at time step i.
2. hi and ci = LSTM maintains two states (‘h’ for hidden state and ‘c’ for cell state) at each time step. Combined together these are internal state of the LSTM at time step i.
3. Yi = Output sequence at time step i. Yi is actually a probability distribution over the entire vocabulary which is generated by using a softmax activation. Thus each Yi is a vector of size “vocab_size” representing a probability distribution.
We will add two tokens in the output sequence as follows:
“START_ John is hard working _END”.
The most important point is that the initial states (h0, c0) of the decoder are set to the final states of the encoder. This intuitively means that the decoder is trained to start generating the output sequence depending on the information encoded by the encoder.
Finally, the loss is calculated on the predicted outputs from each time step and the errors are backpropagated through time in order to update the parameters of the network. Training the network over a longer period with a sufficiently large amount of data results in pretty good predictions.
There are two primary drawbacks to this architecture, both related to length.
Furthermore, for more robust and lengthy sentences we have models like Attention Models and Transformers.
Here this is my GitHub repository for complete word level as well as character level encoder-decoder Model.
The document examines Sequence to Sequence models, their Encoder-Decoder architecture, and applications, while acknowledging their limitations, especially with long sequences. Despite drawbacks, such as struggles with lengthy sequences, their impact on fields like machine translation remains significant, underscoring the necessity for further research to boost efficiency.
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Hi Prasoon Very well explained LSTM and Seq2Seq Congrats