Abstract: Techniques for machine learning (ML) and natural language processing (NLP) are described. One technique enables the creation of a clean training dataset through just a few API calls. Another technique provides an automated process for generating a domain-specific lexicon, which is then used to generate ML training datasets, in a manner that requires little to no human labor. Another technique gathers ML training data from domain-specific public sources, which are more likely than typical public sources to contain focused terminology and to be free from errors, thus resulting in trained ML models that provide more accurate inferences.

Abstract: Prompt discovery is performed for identifying prompts to natural language processing machine learning models. A request to determine a prompt for a natural language processing task performed by a pre-trained natural language processing machine learning model may be received. A task classification for the natural language processing task may be determined and candidate prompts for the natural language processing prompt task collection selected. Respective prompt results for the candidate prompts are evaluated to generate a prompt recommendation for the natural language processing task.

Abstract: Prompt development techniques are implemented for tuning natural language processing machine learning models using selected prompts from a prompt task collection. A prompt development system may support requests to further adapt a pre-trained natural language processing machine learning model to tune the pre-trained natural language processing machine learning model for use with a selected prompt. Evaluation of the tuned natural language processing machine learning model may be performed and provided as a result.

Abstract: Subsets of training data are selected for iterations of a statistical model through a training process. The selection can reduce the amount of data to be processed by selecting the training data that will likely have significant training value for the pass. This can include using a metric such as the loss or certainty to sample the data, such that easy to classify instances are used for training less frequently than harder to classify instances. A cutoff value or threshold can also, or alternatively, be used such that harder to classify instances are not selected for training until later in the process when the model may be more likely to benefit from training on those instances. Sampling can vary between passes for variety, and the cutoff value might also change such that all data instances are eligible for training selection by at least the last iteration.

Abstract: A keyphrase extraction service implements techniques for determining a set of keyphrases associated with set of words. A word is selected from the set of words and a neural model is used to determine a label for the word based on features of the word and labels corresponding to other words of the set of words. The set of keyphrases is determined from the labels associated with the set of words.