Boosting Conversational AI Correctness by Accounting for ASR Errors Using a Sequence to Sequence Model

Abstract

This paper describes the winning submission to the challenge CAICCAIC: Center for Artificial Intelligence Challenge on Conversational AI Correctness. The aim of the challenge was to design a mechanism of natural language understanding capable of interpreting user prompts. The prompts were the output of an automatic speech recognition system and therefore contained errors. In this scenario, it was necessary to apply techniques of accounting for these errors. As per the results of the challenge, the most effective technique proved to be an original use of a sequence to sequence model. The key idea was the concatenation of labels before passing them to the model for training and prediction.