Text Style Transfer: Controlling Attributes of Generated Text
What is Text Style Transfer?
Text Style Transfer is a task that involves changing certain attributes, such as sentiment, in generated text. This can be useful in various applications like generating reviews or product descriptions with a particular tone, or creating content that matches a certain style. In simple terms, we can say that Text Style Transfer involves making a piece of text written in one style appear as though it was written in another style.
How is Text Style Transfer Achieved?
The two main types of Text Style Transfer techniques are parallel and non-parallel data methods. Parallel data methods are supervised approaches that use a neural sequence-to-sequence model. This model uses an encoder-decoder architecture to generate text with specific attributes. Methods on non-parallel data, on the other hand, are unsupervised techniques that use Disentanglement, Prototype Editing, and Pseudo-Parallel Corpus Construction to achieve Text Style Transfer.
Methods of Transfer through Parallel Data
Parallel data methods use a neural network to translate or transfer text from the original style to a desired new style. The neural network model consists of an encoder that encodes the input text into a vector representation, which is then decoded by the decoder to produce the new style text. There are several models used for Text Style Transfer such as Conditional Variational Autoencoders (CVAE), Dynamic Memory Networks (DMN), and Adversarial Training (AT).
Methods of Transfer through Non-Parallel Data
In non-parallel data methods, Text Style Transfer is achieved without the use of parallel texts. In these methods, the focus is on disentangling the content from the style to facilitate Text Style Transfer. Prototype Editing involves altering a prototype text that represents the desired style. Pseudo-Parallel Corpus Construction creates a synthetic parallel data set by sampling sentences with desirable attributes from a large enough corpus. Disentanglement separates the content and style of the input text through a process called attribute swapping, allowing models to learn which aspects of an input affect its style.
Popular Benchmark Dataset & Model Evaluation
The popular benchmark dataset for Text Style Transfer is the Yelp Review Dataset, which consists of reviews on different businesses. The attributes evaluated in this data set are primarily sentiment classification, with the desired sentiment of a text being either positive or negative. Models are evaluated based on Sentiment Accuracy, Bilingual Evaluation Understudy (BLEU), and Perplexity (PPL) metrics. Sentiment Accuracy measures how many reviews are correctly classified, BLEU evaluates the grammatical correctness of the output text while PPL evaluates the coherence and fluency of the generated text.
Applications of Text Style Transfer
Text Style Transfer has several applications in the field of Natural Language Processing (NLP) such as, creating machine translations, making chatbots speak in a particular tone, improving the quality of text summarization, and creating personalized marketing campaigns by generating product descriptions in a desired style. Text Style Transfer can also be useful for generating content for social media platforms in a particular style, or to help creative writers generate different styles for their work.
Text Style Transfer is an evolving field, with increasing interest and potential applications in the field of NLP. Parallel and non-parallel data methods are used to achieve Text Style Transfer. The benchmark for the evaluation of Text Style Transfer models is the Yelp Dataset, which is evaluated using Sentiment Accuracy, BLEU, and PPL metrics. Text Style Transfer has numerous applications such as machine translations, chatbot interfaces, personalized marketing, social media content creation, and creative writing. As Text Style Transfer gains more attention in the NLP community, we can expect to see advancements in theories and techniques that make Text Style Transfer even more powerful and practical.
