Papers by Michael Heck
Dynamic Dialogue Policy for Continual Reinforcement Learning (2022.coling-1)
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Christian Geishauser, Carel van Niekerk, Hsien-chin Lin, Nurul Lubis, Michael Heck, Shutong Feng, Milica Gašić
| Challenge: | Continual reinforcement learning of the dialogue policy has remained unaddressed . lack of a framework with training protocols, baseline models and suitable metrics has hindered research in this direction. |
| Approach: | They propose a continual learning algorithm, baseline architectures and metrics for assessing continual reinforcement learning models. |
| Outcome: | The proposed architecture can integrate new knowledge seamlessly and achieve significant zero-shot performance when exposed to unseen domains. |
Grounding Open-Domain Instructions to Automate Web Support Tasks (2021.naacl-main)
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| Challenge: | RUSS is a task and dataset to ground natural language instructions on the web to perform previously unseen tasks. |
| Approach: | They build a task and dataset to ground AI agents from open-domain, step-by-step instructions on the web. |
| Outcome: | The proposed model outperforms existing models that map instructions to actions without WebLang. |
Robust Dialogue State Tracking with Weak Supervision and Sparse Data (2022.tacl-1)
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Michael Heck, Nurul Lubis, Carel van Niekerk, Shutong Feng, Christian Geishauser, Hsien-Chin Lin, Milica Gašić
| Challenge: | Generalizing dialogue state tracking (DST) to new data and domains is especially challenging due to the strong reliance on abundant and fine-grained supervision during training. |
| Approach: | They propose a training strategy to build extractive DST models without the need for fine-grained manual span labels. |
| Outcome: | The proposed model improves robustness against sample sparsity, new concepts, and topics, leading to state-of-the-art performance on a range of benchmarks. |
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. |
A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction (2025.tacl-1)
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Carel van Niekerk, Christian Geishauser, Michael Heck, Shutong Feng, Hsien-chin Lin, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Milica Gašić
| Challenge: | Existing approaches to training deep neural networks require large amounts of meticulously annotated data. |
| Approach: | They propose a pool-based active learning framework that requires expert annotators to label only a fraction of a sequence and facilitates self-supervision for the remainder of the sequence. |
| Outcome: | The proposed model outperforms baselines on dialogue belief tracking tasks. |
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format (2023.emnlp-demo)
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Qi Zhu, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Shutong Feng, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gasic, Minlie Huang
| Challenge: | Existing tools for building TOD systems often lack a user-friendly interface . a toolkit with advanced, easily integrable modules is needed to bridge this gap . |
| Approach: | They propose a multifaceted dialogue system toolkit that integrates diverse datasets and models with a streamlined training process and in-depth evaluation tools. |
| Outcome: | The proposed toolkit combines RL and transfer learning to support the rapid development and evaluation of robust dialogue policies. |
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. |
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems (2022.lrec-1)
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Shutong Feng, Nurul Lubis, Christian Geishauser, Hsien-chin Lin, Michael Heck, Carel van Niekerk, Milica Gasic
| Challenge: | Existing emotion-annotated task-oriented corpora are limited in size, label richness, and public availability, creating a bottleneck for downstream tasks. |
| Approach: | They propose a large-scale manually emotion-annotated corpus of task-oriented dialogues based on a multi-domain task-orientated dataset. |
| Outcome: | The proposed method is based on a task-oriented dialogue dataset with 11K dialogues and 83K emotion annotations of user utterances. |
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity? (2023.acl-short)
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Michael Heck, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Shutong Feng, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Milica Gasic
| Challenge: | Recent research on dialog state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas. |
| Approach: | They propose to use schema descriptions to facilitate zero-shot transfer to new domains . they argue that general purpose language models lack the ability to replace specialized systems . |
| Outcome: | The proposed method achieves state-of-the-art in zero-shot DST with in-context learning capabilities. |
Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance (2021.emnlp-main)
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Carel van Niekerk, Andrey Malinin, Christian Geishauser, Michael Heck, Hsien-chin Lin, Nurul Lubis, Shutong Feng, Milica Gasic
| Challenge: | Neural dialogue belief trackers that take uncertainty into account are often overconfident in their decisions and therefore less robust. |
| Approach: | They propose to use different uncertainty measures in neural belief tracking to integrate uncertainty into the feature space of the policy and train policies through interaction with a user simulator. |
| Outcome: | The proposed approach improves both performance and robustness of the downstream dialogue policy. |
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 . |