Among the best real-life applications of Natural Language Processing or NLP, text generation deserves special attention. Deep learning technology can be utilized to perform multiple text generation tasks such as composing music, writing poems, or even generating movie scripts.
What is deep learning text generation?
Deep learning text generation is a process in which you instruct a deep learning model to generate purposeful text in an uncomplicated manner. It is regarded as a subcategory in the field of Natural Language Processing. Using artificial intelligence and computational linguistics it can automatically initiate the process of natural language text generation in order to satisfy specific communicative needs.
How does deep learning text generation work?
At first, you need to use a corpus to coach the Language Model or LM. In this circumstance, the LM will be able to learn and find out what will be the statistical distribution of the very next token if it already has a token sequence from the corpus.
Here is a step-by-step process on how deep learning text generation works in general:
- The LM operates in a loop when used for text generation
- We can issue a seed or initial random text to the LM and the LM will predict the next token.
- Now we need to connect the predicted token with the seed and again issue this sequence to the LM as a new seed and the process will continue like this.
Character level text generation
You can instruct a Language Model to produce text on a character-by-character basis and in this case, all the inputs and outputs tokens will be regarded as a character. More importantly, the Language Model will produce a probability distribution on different characters.
Word level text generation
Just like Character level text generation, you can also train your Language Model to produce text on a word-by-word basis. The input and output token in this case will be a word and the probability distribution focuses on the vocabulary.
Which one should you choose for your project?
The character-level Language Models are more expensive in terms of computational power and they can grammatically correct the sequences for a variety of languages. It is especially applicable for languages that have a huge hidden layer.
On the other hand, word-level Language Models can be trained quickly and can produce better logical texts although they are far from creating a proper sense.
The primary distinction between the character level Language Models and the word level Language Models are:
- The vocabulary of the character-level Language Model is very small. For example, the dataset of GBW can contain around 800 characters in comparison to 800,000 words.
- During the preprocessing step the character level model doesn’t require tokenization.
- The character-level Language Models have quicker inference and need less memory in comparison to the word-level Language Models.
- To successfully run the character-level Language Models for a longer term you need to provide it with a big hidden layer which in turn will increase your computational cost.
To properly understand the advantages and disadvantages of both machine learning text generation Language Models you will need to regularly work with them.
Different kinds of Language Models available in Artificial Neural Networks
Recurrent Neural Networks
RNN or Recurrent Neural Networks are one of the most useful algorithms to tackle Natural Language processing problems and are especially applicable while processing sequential data. The RNN has internal memory with which it can recollect the previous input and the current input which ultimately leads to trouble-free sequence modeling.
Also known as sequence-to-sequence models, it has been developed for machine translation. Even though it has simple architecture it works pretty efficiently.
Generative Adversarial Networks
Generative Adversarial Networks or GANs can create practical samples by utilizing an adversary. The job of this discriminator network (adversary) is to find out if the sample produced is real or not.
To use the text data in any deep learning model, we have to transform the text into numbers. Nevertheless, in the deep learning model if you pass on big enough sparse vectors it will greatly affect the deep learning models which are very dissimilar to the machine learning models. That is why it is better to convert the texts into small but dense vectors and word embedding can help us transfigure the text into dense vectors.