Transformer models: Encoder-Decoders

Understanding Encoder-Decoder Architecture

This video explains the encoder-decoder architecture and how it works. It also highlights the importance of understanding encoders and decoders as standalone models before studying the encoder-decoder model.

Encoder

• The encoder takes words as inputs, casts them through the encoder, and retrieves a numerical representation for each word cast through it.

• The numerical representation holds information about the meaning of the sequence.

Decoder

• The decoder is used in a manner that we haven't seen before.

• We pass the outputs of the encoder directly to it along with an initial sequence.

• Using this input alongside its usual sequence input, the decoder will take a stab at decoding the sequence.

Encoder-Decoder Magic

• The encoder accepts a sequence as input, computes a prediction, and outputs a numerical representation. Then, it sends that over to the decoder.

• The decoder decodes what the encoder has output using this input alongside its usual sequence input.

Auto-regressive Decoding

• Once we have both feature vector and an initial generated word, we don't need the encoder anymore.

• As we have seen before with the decoder, it can act in an auto-regressive manner;the word it has just output can now be used as an input.

Translation Language Modeling Example

• We use an example of translating "Welcome to NYC" from English to French using a transformer model trained for that task explicitly.

• We use start of sequence word to ask decoder to output first word which is "Bienvenue" meaning "Welcome".

• We then use "Bienvenue" as the input sequence for the decoder. This, alongside the feature vector, allows the decoder to predict the second word, "à", which is "to" in English.

• Finally, we ask the decoder to predict a third word; it predicts "NYC", which is correct.

Importance of Encoder-Decoder Architecture

• Encoders-decoders are special because they're able to manage sequence-to-sequence tasks like translation.

• The encoder and decoder often do not share weights. Therefore, we have an entire block (the encoder) that can be trained to understand the sequence and extract relevant information.

Sequence to Sequence Transformers

This section discusses the use of sequence to sequence transformers in natural language processing tasks such as translation and summarization.

Translation

• Transformers can be used for translating sequences of words from one language to another.

• A decoder can generate translations in an auto-regressive manner.

Summarization

• Transformers are useful for summarizing long sequences of text.

• The encoder and decoder can have different context lengths, allowing for a smaller context for the summarized sequence.

Encoder-Decoder Models

• There are many types of encoder-decoder models available in the transformers library.

• Specific encoders and decoders can be chosen based on their performance on specific tasks.