1 code implementation • EMNLP 2021 • Ting-Wei Wu, Ruolin Su, Biing Juang
We show that it successfully extends to few/zero-shot setting where part of intent labels are unseen in training data, by also taking account of semantics in these unseen intent labels.
no code implementations • 10 Nov 2023 • Ruolin Su, Ting-Wei Wu, Biing-Hwang Juang
Tracking dialogue states is an essential topic in task-oriented dialogue systems, which involve filling in the necessary information in pre-defined slots corresponding to a schema.
no code implementations • 26 Feb 2023 • Ruolin Su, Zhongkai Sun, Sixing Lu, Chengyuan Ma, Chenlei Guo
Recent advances in cross-lingual commonsense reasoning (CSR) are facilitated by the development of multilingual pre-trained models (mPTMs).
1 code implementation • 25 Feb 2023 • Ruolin Su, Jingfeng Yang, Ting-Wei Wu, Biing-Hwang Juang
With the demanding need for deploying dialogue systems in new domains with less cost, zero-shot dialogue state tracking (DST), which tracks user's requirements in task-oriented dialogues without training on desired domains, draws attention increasingly.
1 code implementation • 4 Aug 2022 • Ruolin Su, Ting-Wei Wu, Biing-Hwang Juang
As an essential component in task-oriented dialogue systems, dialogue state tracking (DST) aims to track human-machine interactions and generate state representations for managing the dialogue.
no code implementations • 29 Jun 2022 • Ruolin Su, Xiao Liu, Sotirios A. Tsaftaris
With the advent of AI learned on data, one can imagine that such rights can extent to requests for forgetting knowledge of patient's data within AI models.
1 code implementation • 3 Sep 2021 • Ting-Wei Wu, Ruolin Su, Biing-Hwang Juang
The success of interactive dialog systems is usually associated with the quality of the spoken language understanding (SLU) task, which mainly identifies the corresponding dialog acts and slot values in each turn.