Predicting Students’ Difficulties From a Piece of Code

no code implementations • IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2021 • Marco Moresi, Marcos J. G
omez, and Luciana Benotti

In this article, we compare two machine learning models that predict whether students need help regardless of whether their code compiles or not.

LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization

1 code implementation • COLING 2020 • Nurul Lubis, Christian Geishauser, Michael Heck, Hsien-Chin Lin, Marco Moresi, Carel van Niekerk, Milica Gašić

In this paper, we explore three ways of leveraging an auxiliary task to shape the latent variable distribution: via pre-training, to obtain an informed prior, and via multitask learning.

Decision Making Reinforcement Learning (RL) +1

Out-of-Task Training for Dialog State Tracking Models

no code implementations • COLING 2020 • Michael Heck, Carel van Niekerk, Nurul Lubis, Christian Geishauser, Hsien-Chin Lin, Marco Moresi, Milica Gašić

Dialog state tracking (DST) suffers from severe data sparsity.

dialog state tracking Multi-Task Learning +1

Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles

no code implementations • Findings of the Association for Computational Linguistics 2020 • Carel van Niekerk, Michael Heck, Christian Geishauser, Hsien-Chin Lin, Nurul Lubis, Marco Moresi, Milica Gašić

The ability to accurately track what happens during a conversation is essential for the performance of a dialogue system.

TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking

no code implementations • SIGDIAL (ACL) 2020 • Michael Heck, Carel van Niekerk, Nurul Lubis, Christian Geishauser, Hsien-Chin Lin, Marco Moresi, Milica Gašić

In this paper we present a new approach to DST which makes use of various copy mechanisms to fill slots with values.

dialog state tracking Multi-domain Dialogue State Tracking +2