Spoken language understanding from unaligned data using discriminative classification models

By François Mairesse, Milica Gasic, Filip Jurcícek, Simon Keizer, Blaise Thomson, Kai Yu, Susannah Young. 2009

117 citations recorded by the source corpus.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jianfeng Gao
2. Minlie Huang
3. Bing Liu
4. Li Deng
5. Jason Aaron Edward Weston
6. Richard Socher
7. Xiaoyan Zhu
8. Dilek Z. Hakkani-Tür

Citations in the corpus

• Neural Belief Tracker: Data-Driven Dialogue State Tracking (2016, methodology): “These are used as input to the final decision-making module which decides whether the user expressed the intent represented by the current candidate slot-value pair.”
• ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems (2020, methodology): “vides three models: Semantic Tuple Classifier (STC) (Mairesse et al., 2009), MILU (Lee et al.”
• Joint semantic utterance classification and slot filling with recursive neural networks (2014, methodology): “[16] used SVM classifiers to parse inputs, and apply semantic labels the interior nodes, resulting in both slot and intent labels.”