A piece of data is a symbolic representation of a quantitative or qualitative attribute or variable; that is to say, it is a unit of information.
Nowadays, the need to use large amounts of quality data in order to get closer to human parity in the field of artificial intelligence (AI) is an irrefutable fact. Although there is an increasing amount of data, processing and cleaning them in order to use them for training is a costly process and often leads to discarding a lot of them, besides the fact that their quality cannot be assured.
The main purpose of using data augmentation techniques is to improve the diversity of the training data set, as well as to help the model improve the generalization of test data that it has not been in contact with during training. Therefore, it is of extreme relevance when it comes to obtaining large amounts of data, and quality data, in order to achieve AI models that produce relevant outputs.
Data augmentation is widely employed in the field of computer vision. It can also be used to improve certain tasks in natural language processing (NLP), although the procedure is more complex due to the nature of this area.
Various data augmentation techniques are employed in NLP with the goal of diversifying the data and helping to improve AI models for different tasks and domains. These are some of the techniques that can be used:
Paraphrasing. Paraphrasing methods generate augmented data that have a limited semantic difference from the original data, based on appropriate and restricted changes in the sentences. The augmented data convey information very similar to the original.
Adding noise. These methods add discrete or continuous noise under the premise of guaranteeing validity. The objective of such methods is to improve the robustness of the model.
Sampling. Methods based on sampling grasp the data distribution and sample new data within it. They produce more diverse data and satisfy more downstream task needs based on artificial heuristics and trained models.
At Pangeanic, we are working towards a robust data augmentation system in the NLP field, with the goal of generating monolingual and bilingual corpora. For this reason, we develop, research, and experiment with different techniques to find the ones that best suit our needs.
Due to the fact that data is believed to define the quality of the models, we are investing time and effort into generating new quality data.
Pangeanic is your language processing company. We develop and implement our own technology, combining the best of artificial and human intelligence to offer solutions that perfectly fit the market.