Search Results for author: Shutong Feng

Found 14 papers, 1 papers with code

Speech-based Slot Filling using Large Language Models

no code implementations13 Nov 2023 Guangzhi Sun, Shutong Feng, Dongcheng Jiang, Chao Zhang, Milica Gašić, Philip C. Woodland

Recently, advancements in large language models (LLMs) have shown an unprecedented ability across various language tasks.

In-Context Learning slot-filling +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

Affect Recognition in Conversations Using Large Language Models

no code implementations22 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

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

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

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