Sequence to Sequence Modeling

Overview

Sequence to Sequence (S2S) modeling is a type of neural network architecture that is commonly used in various natural language processing (NLP) applications. This approach is especially useful for problems where the input and output are sequences of different lengths, such as machine translation, text summarization, and speech recognition. In this article, we will explore the basics of Sequence to Sequence modeling and its applications.

Language translation is the most common application of sequence to sequence modeling when it first became popular.

Google Translate, Yandex Translate, DeepL Translator, Bing Microsoft Translator

In addition to language translation code translation is another popular area for the use of S2S modeling as seen in facebooks article on programming language translation.

Deep learning to translate between programming languages

The key is to understand that the S2S modeling can mean any general sequence to any sequence of tokens.

As a note: In sequence to sequence modeling, a token refers to a discrete unit of information in a sequence, such as a word,character or a piece of code.

The mathematics of Sequence to Sequence

An input sequence

x1, x2, x3, ..., xm

and an output sequence (which may be of a different length) 

y1,y2,...,yn

Find the target sequence that maximizes the conditional probability

The three questions that we need to answers:

1) What does the model p(y|x,theta) do look like?

2) What are the parameters?

3) How do we find the argmax?

Sequence to Sequence (Seq2Seq) modeling is a neural network architecture that aims to map an input sequence to an output sequence. In this approach, the input and output sequences can be of different lengths, and the model is designed to handle such variability. The basic idea of Seq2Seq modeling is to use an encoder to encode the input sequence into a fixed-length representation, which is then fed to a decoder to generate the output sequence.

The encoder takes the input sequence as a sequence of vectors and processes them one by one, typically using a recurrent neural network (RNN). The output of the encoder is a single fixed-length vector, which is considered as the encoded representation of the input sequence. The decoder, on the other hand, takes this encoded representation and generates the output sequence, again typically using an RNN. The decoder generates each element of the output sequence by conditioning on the previous elements it has generated and the encoded representation of the input sequence.

Applications of Sequence to Sequence Modeling

Seq2Seq modeling has become popular in NLP applications such as machine translation, text summarization, and speech recognition. Here are some examples of how Seq2Seq modeling is used in these applications:

1. Machine Translation: In machine translation, the input is a sequence of words in one language, and the output is a sequence of words in another language. Seq2Seq modeling is used to learn the mapping between the two languages by training on parallel corpora. The encoder takes the source language sentence as input and generates a fixed-length representation, which is then fed to the decoder to generate the target language sentence.
   
2. Text Summarization: Text summarization is the task of generating a shorter version of a given text. Seq2Seq modeling is used to learn how to generate a summary of a given text by training on pairs of long and short versions of the same text. The encoder takes the long version of the text as input and generates a fixed-length representation, which is then fed to the decoder to generate the summary.
   
3. Speech Recognition: In speech recognition, the input is an audio sequence, and the output is a sequence of words. Seq2Seq modeling is used to learn how to transcribe the audio sequence into a sequence of words. The encoder takes the audio sequence as input and generates a fixed-length representation, which is then fed to the decoder to generate the sequence of words.

Seq2Seq modeling is a powerful neural network architecture that has been widely used in various NLP applications. It allows for handling input and output sequences of different lengths and can learn to generate output sequences by conditioning on the input sequence. Seq2Seq modeling has shown impressive results in machine translation, text summarization, and speech recognition, and is likely to continue to be a popular approach in future NLP research.