Papers with contrastive framework

3 papers
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.
A Simple Angle-based Approach for Contrastive Learning of Unsupervised Sentence Representation (2024.findings-emnlp)

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Challenge: a promising baseline SimCSE has made notable breakthroughs in unsupervised SRL . however, there is still room for designing a novel contrastive framework specifically targeted for SRL.
Approach: They propose an angle-based similarity function for a contrastive objective and propose a new approach for SRL.
Outcome: The proposed approach shows better training dynamics on SRL than the standard cosine similarity function.
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue (2023.acl-long)

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Challenge: Existing pre-trained language models rely on a contrastive framework and are difficult to use in practice.
Approach: They propose a dialogue pre-training model which distills future knowledge to the representation of the previous dialogue context using a self-training framework.
Outcome: The proposed model can be applied to various downstream dialogue tasks.

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