Papers by Marco Moresi
Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles (2020.findings-emnlp)
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Carel van Niekerk, Michael Heck, Christian Geishauser, Hsien-chin Lin, Nurul Lubis, Marco Moresi, Milica Gasic
| Challenge: | Current models for dialogue state tracking only achieve 55% accuracy . however, they lack in performance compared to belief trackers and do not produce well calibrated distributions. |
| Approach: | They propose to calibrate a model for dialogue belief trackers to measure dialogue state accuracy. |
| Outcome: | The proposed model outperforms existing models in terms of accuracy and accuracy. |
Out-of-Task Training for Dialog State Tracking Models (2020.coling-main)
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Michael Heck, Christian Geishauser, Hsien-chin Lin, Nurul Lubis, Marco Moresi, Carel van Niekerk, Milica Gasic
| Challenge: | Dialog state tracking (DST) suffers from data sparsity. |
| Approach: | They utilize non-dialog data from unrelated NLP tasks to train dialog state trackers . they propose to use dialog state tracking to summarise the conversation history . |
| Outcome: | The proposed method exploits non-dialog data from unrelated NLP tasks to train dialog state trackers. |
LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization (2020.coling-main)
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Nurul Lubis, Christian Geishauser, Michael Heck, Hsien-chin Lin, Marco Moresi, Carel van Niekerk, Milica Gasic
| Challenge: | Reinforcement learning (RL) can be used to steer a conversation towards successful task completion. |
| Approach: | They propose to use latent latent variables to shape latent variable distributions . they use response auto-encoding as auxiliary task to capture generative factors . |
| Outcome: | The proposed approach yields a more action-characterized latent representations . the proposed approach achieves state-of-the-art success rates . |