Papers with TOD baselines

2 papers
DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations (2024.findings-naacl)

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Challenge: Existing language models pre-trained on general text overlook the one-to-many property of task-oriented dialogues, where multiple responses can be appropriate given the same context.
Approach: They propose a model that pre-trains LLMs to learn diverse task-oriented dialogue representations by removing domain knowledge that contradicts TODs.
Outcome: The proposed model outperforms strong TOD baselines on various downstream dialogue tasks and learns the intrinsic diversity of task-oriented dialogues.
BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses (2024.lrec-main)

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Challenge: Existing pre-trained language models lack diversity and linguistic challenges in task-oriented dialogues.
Approach: They propose a self-bootstrapping dialogue pre-training model called BootTOD . it learns task-oriented dialogue representations via a framework .
Outcome: The proposed model outperforms strong TOD baselines on diverse dialogue tasks.

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