Presenting at INTERSPEECH 2023 🇮🇪

Aug 1, 2023 Conference, Talk

Time	Location	Activity
Aug 24 Thu, 10:00-10:20 (IST)	Wicklow Hall 1	Oral presentation of the conf paper: Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning. In the collaboration work with Amazon Alexa AI, we introduce a dialogue state tracking model tuning less than 1% of LM parameters and achieves better low-resource performance with prompt tuning techniques.

Presenting Papers

Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning

Mingyu Derek Ma, Jiun-Yu Kao, Shuyang Gao, Arpit Gupta, Di Jin, Tagyoung Chung, Nanyun Peng

INTERSPEECH, 2023 & ENLSP at NeurIPS, 2022

We use soft prompt tokens to learn task properties, incorporate segment information and reiterate the task before predicting value. Our method drastically reduces the number of parameters needed to less than 0.5% of prior works while achieving better low-resource dialogue state tracking performance.

INTERSPEECH Conference