no code implementations • 13 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.
no code implementations • 22 Sep 2023 • Shutong Feng, Guangzhi Sun, Nurul Lubis, Chao Zhang, Milica Gašić
This study delves into the capacity of large language models (LLMs) to recognise human affect in conversations, with a focus on both open-domain chit-chat dialogues and task-oriented dialogues.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 24 Aug 2023 • Shutong Feng, Nurul Lubis, Benjamin Ruppik, Christian Geishauser, Michael Heck, Hsien-Chin Lin, Carel van Niekerk, Renato Vukovic, Milica Gašić
Our framework yields significant improvements for a range of chit-chat ERC models on EmoWOZ, a large-scale dataset for user emotion in ToDs.
no code implementations • 2 Jun 2023 • Michael Heck, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Shutong Feng, Christian Geishauser, Hsien-Chin Lin, Carel van Niekerk, Milica Gašić
Recent research on dialogue state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas.
no code implementations • 2 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.
1 code implementation • 30 Nov 2022 • Qi Zhu, Christian Geishauser, Hsien-Chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gašić, Minlie Huang
Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants.
no code implementations • SIGDIAL (ACL) 2022 • Nurul Lubis, Christian Geishauser, Hsien-Chin Lin, Carel van Niekerk, Michael Heck, Shutong Feng, Milica Gašić
They are ideally evaluated with human users, which however is unattainable to do at every iteration of the development phase.
no code implementations • SIGDIAL (ACL) 2022 • Hsien-Chin Lin, Christian Geishauser, Shutong Feng, Nurul Lubis, Carel van Niekerk, Michael Heck, Milica Gašić
In addition, its behaviour can be further shaped with reinforcement learning opening the door to training specialised user simulators.
no code implementations • COLING 2022 • Christian Geishauser, Carel van Niekerk, Nurul Lubis, Michael Heck, Hsien-Chin Lin, Shutong Feng, Milica Gašić
The lack of a framework with training protocols, baseline models and suitable metrics, has so far hindered research in this direction.
no code implementations • 7 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.
no code implementations • 15 Sep 2021 • Christian Geishauser, Songbo Hu, Hsien-Chin Lin, Nurul Lubis, Michael Heck, Shutong Feng, Carel van Niekerk, Milica Gašić
The dialogue management component of a task-oriented dialogue system is typically optimised via reinforcement learning (RL).
1 code implementation • LREC 2022 • Shutong Feng, Nurul Lubis, Christian Geishauser, Hsien-Chin Lin, Michael Heck, Carel van Niekerk, Milica Gašić
We report a set of experimental results to show the usability of this corpus for emotion recognition and state tracking in task-oriented dialogues.
Ranked #1 on Emotion Recognition in Conversation on EmoWoz
Emotion Recognition in Conversation Task-Oriented Dialogue Systems
no code implementations • EMNLP 2021 • Carel van Niekerk, Andrey Malinin, Christian Geishauser, Michael Heck, Hsien-Chin Lin, Nurul Lubis, Shutong Feng, Milica Gašić
This highlights the importance of developing neural dialogue belief trackers that take uncertainty into account.
no code implementations • SIGDIAL (ACL) 2021 • Hsien-Chin Lin, Nurul Lubis, Songbo Hu, Carel van Niekerk, Christian Geishauser, Michael Heck, Shutong Feng, Milica Gašić
TUS can compete with rule-based user simulators on pre-defined domains and is able to generalise to unseen domains in a zero-shot fashion.
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.
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.
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.
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.
Ranked #11 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.1
dialog state tracking Multi-domain Dialogue State Tracking +2
no code implementations • WS 2018 • Nurul Lubis, Sakriani Sakti, Koichiro Yoshino, Satoshi Nakamura
Positive emotion elicitation seeks to improve user{'}s emotional state through dialogue system interaction, where a chat-based scenario is layered with an implicit goal to address user{'}s emotional needs.
no code implementations • LREC 2016 • Nurul Lubis, R Gomez, y, Sakriani Sakti, Keisuke Nakamura, Koichiro Yoshino, Satoshi Nakamura, Kazuhiro Nakadai
Emotional aspects play a vital role in making human communication a rich and dynamic experience.