Search Results for author: Michael Heck

Found 18 papers, 3 papers with code

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

Robust Dialogue State Tracking with Weak Supervision and Sparse Data

no code implementations7 Feb 2022 Michael Heck, Nurul Lubis, Carel van Niekerk, Shutong Feng, Christian Geishauser, Hsien-Chin Lin, Milica Gašić

Our architecture and training strategies improve robustness towards sample sparsity, new concepts and topics, leading to state-of-the-art performance on a range of benchmarks.

Dialogue State Tracking

Dialogue Term Extraction using Transfer Learning and Topological Data Analysis

no code implementations SIGDIAL (ACL) 2022 Renato Vukovic, Michael Heck, Benjamin Matthias Ruppik, Carel van Niekerk, Marcus Zibrowius, Milica Gašić

Goal oriented dialogue systems were originally designed as a natural language interface to a fixed data-set of entities that users might inquire about, further described by domain, slots, and values.

Goal-Oriented Dialogue Systems Language Modelling +4

EmoUS: Simulating User Emotions in Task-Oriented Dialogues

no code implementations2 Jun 2023 Hsien-Chin Lin, Shutong Feng, Christian Geishauser, Nurul Lubis, Carel van Niekerk, Michael Heck, Benjamin Ruppik, Renato Vukovic, Milica Gašić

Existing user simulators (USs) for task-oriented dialogue systems only model user behaviour on semantic and natural language levels without considering the user persona and emotions.

Language Modelling Large Language Model +1

CAMELL: Confidence-based Acquisition Model for Efficient Self-supervised Active Learning with Label Validation

no code implementations13 Oct 2023 Carel van Niekerk, Christian Geishauser, Michael Heck, Shutong Feng, Hsien-Chin Lin, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Milica Gašić

Supervised neural approaches are hindered by their dependence on large, meticulously annotated datasets, a requirement that is particularly cumbersome for sequential tasks.

Active Learning

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