Abstract
OBJECTIVES: Recently natural language interfaces (e.g., chatbots) have gained enormous attention. Such interfaces execute underlying application programming interfaces (APIs) based on the user's utterances to perform tasks (e.g., reporting weather). Supervised approaches for building such interfaces rely upon a large set of user utterances paired with APIs. Collecting such pairs is typically starts with obtaining initial utterances for a given API method. Generating initial utterances can be considered as a machine translation task in which an API method is translated into an utterance. However, the key challenge is the lack of training samples for training domain-independent translation models. In this paper, we propose a dataset for training supervised models to generate initial utterances for APIs. DATA DESCRIPTION: The dataset contains 14,370 pairs of API methods and utterances. It is built automatically by converting method descriptions of a large number of APIs to user utterances; and it is cleaned manually to ensure quality. The dataset is also accompanied with a set of microservices (e.g., translating API methods to utterances) which can facilitate the process of collecting training samples for building natural language interfaces.