Year: 2023
Type: conference-paper
Abstract: A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains without using any supervised data -zero-shot domain adaptation.Parameter-Efficient Transfer Learning (PETL) has the potential to address this problem due to its robustness.However, it has yet to be applied to the zero-shot scenarios, as it is not clear how to apply it unsupervisedly.Our method, Prompter, uses descriptions of target domain slots to generate dynamic prefixes that are concatenated to the key and values at each layer's self-attention mechanism.This allows for the use of prefix-tuning in zeroshot.Prompter outperforms previous methods on both the MultiWOZ and SGD benchmarks.In generating prefixes, our analyses find that Prompter not only utilizes the semantics of slot descriptions but also how often the slots appear together in conversation.Moreover, Prompter's gains are due to its improved ability to distinguish "none"-valued dialogue slots, compared against baselines.
Authors: Ibrahim Taha Aksu, Min-Yen Kan, Nancy Chen
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4385569845