Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation

1 code implementation • Findings (EMNLP) 2021 • Jarana Manotumruksa, Jeff Dalton, Edgar Meij, Emine Yilmaz

While state-of-the-art Dialogue State Tracking (DST) models show promising results, all of them rely on a traditional cross-entropy loss function during the training process, which may not be optimal for improving the joint goal accuracy.

Data Augmentation Dialogue State Tracking

MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation

1 code implementation • 1 Apr 2021 • Fanghua Ye, Jarana Manotumruksa, Emine Yilmaz

The annotations in the training set remain unchanged (same as MultiWOZ 2. 1) to elicit robust and noise-resilient model training.

Dialogue State Tracking Task-Oriented Dialogue Systems

Slot Self-Attentive Dialogue State Tracking

1 code implementation • 22 Jan 2021 • Fanghua Ye, Jarana Manotumruksa, Qiang Zhang, Shenghui Li, Emine Yilmaz

Then a stacked slot self-attention is applied on these features to learn the correlations among slots.

Dialogue State Tracking Task-Oriented Dialogue Systems

Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling

1 code implementation • Findings of the Association for Computational Linguistics 2020 • Fanghua Ye, Jarana Manotumruksa, Emine Yilmaz

Semantic hashing is a powerful paradigm for representing texts as compact binary hash codes.