893 research outputs found
ASR error management for improving spoken language understanding
This paper addresses the problem of automatic speech recognition (ASR) error
detection and their use for improving spoken language understanding (SLU)
systems. In this study, the SLU task consists in automatically extracting, from
ASR transcriptions , semantic concepts and concept/values pairs in a e.g
touristic information system. An approach is proposed for enriching the set of
semantic labels with error specific labels and by using a recently proposed
neural approach based on word embeddings to compute well calibrated ASR
confidence measures. Experimental results are reported showing that it is
possible to decrease significantly the Concept/Value Error Rate with a state of
the art system, outperforming previously published results performance on the
same experimental data. It also shown that combining an SLU approach based on
conditional random fields with a neural encoder/decoder attention based
architecture , it is possible to effectively identifying confidence islands and
uncertain semantic output segments useful for deciding appropriate error
handling actions by the dialogue manager strategy .Comment: Interspeech 2017, Aug 2017, Stockholm, Sweden. 201
Adaptive Cognitive Interaction Systems
Adaptive kognitive Interaktionssysteme beobachten und modellieren den Zustand ihres Benutzers und passen das Systemverhalten entsprechend an. Ein solches System besteht aus drei Komponenten: Dem empirischen kognitiven Modell, dem komputationalen kognitiven Modell und dem adaptiven Interaktionsmanager. Die vorliegende Arbeit enthält zahlreiche Beiträge zur Entwicklung dieser Komponenten sowie zu deren Kombination. Die Ergebnisse werden in zahlreichen Benutzerstudien validiert
Proceedings of the 1st joint workshop on Smart Connected and Wearable Things 2016
These are the Proceedings of the 1st joint workshop on Smart Connected and Wearable Things (SCWT'2016, Co-located with IUI 2016). The SCWT workshop integrates the SmartObjects and IoWT workshops. It focusses on the advanced interactions with smart objects in the context of the Internet-of-Things (IoT), and on the increasing popularity of wearables as advanced means to facilitate such interactions
How Does Beam Search improve Span-Level Confidence Estimation in Generative Sequence Labeling?
Sequence labeling is a core task in text understanding for IE/IR systems.
Text generation models have increasingly become the go-to solution for such
tasks (e.g., entity extraction and dialog slot filling). While most research
has focused on the labeling accuracy, a key aspect -- of vital practical
importance -- has slipped through the cracks: understanding model confidence.
More specifically, we lack a principled understanding of how to reliably gauge
the confidence of a model in its predictions for each labeled span. This paper
aims to provide some empirical insights on estimating model confidence for
generative sequence labeling. Most notably, we find that simply using the
decoder's output probabilities \textbf{is not} the best in realizing
well-calibrated confidence estimates. As verified over six public datasets of
different tasks, we show that our proposed approach -- which leverages
statistics from top- predictions by a beam search -- significantly reduces
calibration errors of the predictions of a generative sequence labeling model
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