Programming bots by synthesizing natural language expressions into API invocations

At present, bots are still in their preliminary stages of development. Many are relatively simple, or developed ad-hoc for a very specific use-case. For this reason, they are typically programmed manually, or utilize machine-learning classifiers to interpret a fixed set of user utterances. In realit...

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Bibliographic Details
Published in2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) pp. 832 - 837
Main Authors Zamanirad, Shayan, Benatallah, Boualem, Barukh, Moshe Chai, Casati, Fabio, Rodriguez, Carlos
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2017
Subjects
Business
Computational modeling
Concrete
Machine learning
Meteorology
Natural languages
Programming
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Summary:At present, bots are still in their preliminary stages of development. Many are relatively simple, or developed ad-hoc for a very specific use-case. For this reason, they are typically programmed manually, or utilize machine-learning classifiers to interpret a fixed set of user utterances. In reality, real world conversations with humans require support for dynamically capturing users expressions. Moreover, bots will derive immeasurable value by programming them to invoke APIs for their results. Today, within the Web and Mobile development community, complex applications are being stringed together with a few lines of code - all made possible by APIs. Yet, developers today are not as empowered to program bots in much the same way. To overcome this, we introduce BotBase, a bot programming platform that dynamically synthesizes natural language user expressions into API invocations. Our solution is two faceted: Firstly, we construct an API knowledge graph to encode and evolve APIs; secondly, leveraging the above we apply techniques in NLP, ML and Entity Recognition to perform the required synthesis from natural language user expressions into API calls.
DOI:10.1109/ASE.2017.8115694