WAAC: An End-to-End Web API Automatic Calls Approach for Goal-Oriented Intelligent Services

Abstract

Web API recommendations have recently been studied extensively. However, recommending an API for a service is different than service intelligence. Web API automatic calls are widely used in question–answer dialog applications and service-composed workflow systems to achieve intelligent services. To finish an automatic Web API call not only requires the Web API ID, but also its input parameters. In this paper, we propose an end-to-end Web API automatic calls approach, named WAAC, that translates a goal’s natural language sentences directly to the Web API invoking sequences including its ID and parameters. This end-to-end approach based on the seq2seq encoder–decoder framework, adopts character-level RNN for the Chinese sentences and introduces a copying mechanism to retrieve API parameters. To train the network, a Chinese version dataset of over 1 million natural sentences and API invoking sequence pairs are generated with some manually labeled data and 72 real Web API invoking logs. Experiments obtain a 96% precision on predicting API invoking sequences and show that the character-level RNN and copying mechanism both contribute considerably to achieving a high precision Web API automatic call system for goal-oriented services.