Sequence_to_Sequence model: Encoder and Decoder model for Language Generation


Sequence-to-sequence (Seq2Seq) models, introduced around 2014, revolutionized natural language processing tasks. Before Seq2Seq, tasks like machine translation relied on fixed-length inputs, limiting their effectiveness. Seq2Seq, utilizing recurrent neural networks, transformed these limitations. It enables processing variable-length sequences and produces variable-length outputs.

For instance, translating English to French involves an input sequence (English sentence) and an output sequence (French translation). This paradigm shift superseded earlier methods, allowing the model to capture contextual information and dependencies across sequences.

Today, Seq2Seq models find applications in diverse fields, from language translation, code generation, and summarization to speech recognition. Their flexibility in handling input and output sequences of varying lengths makes them indispensable for tasks demanding context-aware understanding and generation.

This article will delve into the architecture of Seq2Seq models, exploring their inner workings and training processes. We’ll uncover how these models employ recurrent neural networks to process variable-length input sequences and generate corresponding variable-length outputs.

The focus will be on understanding the mechanisms that enable Seq2Seq models to capture intricate dependencies and contextual information, making them effective in tasks like language translation and summarization.

Reference – RNNs

Sequence-to-sequence Architecture

Sequence -to-sequence model is an example of a conditional language model. Language model because the decoder is predicting the next word of the target sentence y. Conditional because its predictions area also conditioned on the source sentence x. it’s essential to understand its two key components: the encoder, responsible for processing input sequences and capturing contextual information, and the decoder, which generates variable-length output sequences based on the encoded information.

1. Encoder:

The encoder architecture in a Seq2Seq model, often based on Recurrent Neural Networks (RNNs) or other variants like Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU), processes the input sequence. Each element (word or token) in the input sequence is sequentially fed into the encoder.

The hidden states of the encoder capture contextual information and dependencies between the elements of the input sequence. This contextual representation is then used to create a fixed-size context vector that summarizes the input sequence’s information.

The context vector serves as a bridge between the input and output sequences, providing a condensed representation of the input for the decoder to generate the corresponding output sequence. This encoding process allows the model to capture the essence of the input sequence in a way that is conducive to generating the desired output.

2. Decoder:

The decoder architecture in a Seq2Seq model, also often based on Recurrent Neural Networks (RNNs) or variants like LSTM or GRU, takes the encoded context vector from the encoder as input. It operates in a sequential manner, generating the output sequence one element at a time.

At each step, the decoder considers the previously generated elements and the context vector to produce the next element in the sequence. The hidden states of the decoder retain information about the generated sequence and the context from the encoder.

The decoding process continues until a specified termination condition is met or a maximum sequence length is reached. The decoder’s output is the final generated sequence, representing the model’s response or translation. The architecture enables the model to leverage the encoded information to generate coherent and contextually relevant output sequences.

Will feed the source code into the encoder RNN, then feed the target sentence into the decoder RNN, final hidden state will be the initial hidden state of the decoder. In Every step of the decoder RNN, it will produce the probability distribution of what comes next that is ŷ, from these we can compute our loss (cross_entrophy loss or negative log-likelihood of the true next word).

Here backpropagation is happening end to end, one end is the losses, the loss function and the other is beginning of the encoder RNN, that is backpropagation flows through the entire system to this single loss.

The difference in training and testing is, In testing we feed the token back in, once we produced end then we have to stop because we can’t feed end in as the initial next step, But in training we don’t feed the thing that you produced into the next step. During training, we feed the target sentence from the corpus, no matter what the decoder predicts on a step, we don’t use that for anything other than computing loss.

Reference:  Embedding

Loss function In RNNs

Application of sequence_to_sequence model:

1. Neural Machine Translation (NMT):

Seq2Seq models are pivotal in language translation, powering platforms like Google Translate. The encoder processes input sentences, creating a context vector. The decoder then generates translated sentences, leveraging the encoded context for accurate language conversion. NMT has revolutionized translation tasks, achieving state-of-the-art results.

2. Text Summarization:

Seq2Seq models are employed to automatically summarize lengthy documents, aiding in information retrieval and comprehension. The encoder comprehends the document, capturing its essence, and the decoder generates a concise summary by attending to salient features extracted during the encoding process. This facilitates efficient content condensation for improved readability.

3. Speech Recognition:

Seq2Seq models are integral to converting spoken language into text, forming the backbone of voice-activated systems like virtual assistants. The encoder processes audio features, extracting meaningful representations, and the decoder generates textual outputs, effectively transcribing spoken content. This technology enhances accessibility and usability in various applications.

4. Conversational AI:

Seq2Seq models power chatbots and virtual assistants, enabling natural and context-aware interactions with users. The encoder interprets user input, capturing intent and context, while the decoder generates appropriate responses, fostering dynamic and coherent conversations. Seq2Seq models contribute to the advancement of conversational AI, making interactions more engaging and responsive.

5. Image Captioning:

Seq2Seq models play a crucial role in generating textual descriptions for images, enhancing accessibility and understanding. The encoder processes visual features extracted from the image, and the decoder generates descriptive captions, linking visual content to linguistic expressions. This application finds utility in fields like content indexing, aiding visually impaired individuals, and enriching multimedia experiences.

Reference: Sequence_to_sequence_learning


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