Trung Bui

Robust Dialog State Tracking for Large Ontologies

The Dialog State Tracking Challenge 4 (DSTC 4) differentiates itself from the previous three editions as follows: the number of slot-value pairs present in the ontology is much larger, no spoken language understanding output is given, and utterances are labeled at the subdialog level. This paper describes a novel dialog state tracking method designed to work robustly under these conditions, using elaborate string matching, coreference resolution tailored for dialogs and a few other improvements. The method can correctly identify many values that are not explicitly present in the utterance. On the final evaluation, our method came in first among 7 competing teams and 24 entries. The F1-score achieved by our method was 9 and 7 percentage points higher than that of the runner-up for the utterance-level evaluation and for the subdialog-level evaluation, respectively.

A Service-Oriented Framework for Big Data-Driven Knowledge Management Systems

Enterprises nowadays are intensifying their efforts to create value through big data initiatives as well as knowledge management systems to outperform their competitors. Big data is considered as a revolution that transforms traditional enterprises into Data-Driven Organizations (DDOs) in which knowledge discovered from big data will be integrated into traditional organizational knowledge to improve decision-making and to facilitate organizational learning. This paper proposes a service-oriented framework for designing a new generation of big data-driven knowledge management systems to help enterprises to promote knowledge development and to obtain more business value from big data. The key artefacts of the framework are presented based on design science research, including constructs, model, and method. The objective of the framework is to promote both knowledge exploration and knowledge exploitation that need to take place simultaneously in DDOs.