Recent work raises concerns about the use of standard splits to compare natural language processing models.
Avaya Conversational Intelligence(ACI) is an end-to-end, cloud-based solution for real-time Spoken Language Understanding for call centers.
In this paper, we present a method for correcting automatic speech recognition (ASR) errors using a finite state transducer (FST) intent recognition framework.
The models are trained on the Fisher corpus which includes punctuation annotation.
In case of F1 scores and Subset Accuracy - data driven approaches were more likely to perform better than random approaches than otherwise in the worst case.
It provides native Python implementations of popular multi-label classification methods alongside a novel framework for label space partitioning and division.
We show that fastgreedy and walktrap community detection methods on weighted label co-occurence graphs are 85-92% more likely to yield better F1 scores than random partitioning.