A Re-Ranker Scheme For Integrating Large Scale NLU Models | IEEE Conference Publication | IEEE Xplore

A Re-Ranker Scheme For Integrating Large Scale NLU Models


Abstract:

Large scale Natural Language Understanding (NLU) systems are typically trained on large quantities of data, requiring a fast and scalable training strategy. A typical des...Show More

Abstract:

Large scale Natural Language Understanding (NLU) systems are typically trained on large quantities of data, requiring a fast and scalable training strategy. A typical design for NLU systems consists of domain-level NLU modules (domain classifier, intent classifier and named entity recognizer). Hypotheses (NLU interpretations consisting of various intent+slot combinations) from these domain specific modules are typically aggregated with another downstream component. The re-ranker integrates outputs from domain-level recognizers, returning a scored list of cross domain hypotheses. An ideal re-ranker will exhibit the following two properties: (a) it should prefer the most relevant hypothesis for the given input as the top hypothesis and, (b) the interpretation scores corresponding to each hypothesis produced by the re-ranker should be calibrated. Calibration allows the final NLU interpretation score to be comparable across domains. We propose a novel re-ranker strategy that addresses these aspects, while also maintaining domain specific modularity. We design optimization loss functions for such a modularized re-ranker and present results on decreasing the top hypothesis error rate as well as maintaining the model calibration. We also experiment with an extension involving training the domain specific re-rankers on datasets curated independently by each domain to allow further asynchronization.
Date of Conference: 18-21 December 2018
Date Added to IEEE Xplore: 14 February 2019
ISBN Information:
Conference Location: Athens, Greece

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