Name
Title | ||
|---|---|---|
ADPL: Adversarial Prompt-based Domain Adaptation for Dialogue Summarization with Knowledge Disentanglement |
Abstract | ||
|---|---|---|
Traditional dialogue summarization models rely on a large-scale manually-labeled corpus, lacking generalization ability to new domains, and domain adaptation from a labeled source domain to an unlabeled target domain is important in practical summarization scenarios. However, existing domain adaptation works in dialogue summarization generally require large-scale pre-training using extensive external data. To explore the lightweight fine-tuning methods, in this paper, we propose an efficient Adversarial Disentangled Prompt Learning (ADPL) model for domain adaptation in dialogue summarization. We introduce three kinds of prompts including domain-invariant prompt (DIP), domain-specific prompt (DSP), and task-oriented prompt (TOP). DIP aims to disentangle and transfer the shared knowledge from the source domain and target domain in an adversarial way, which improves the accuracy of prediction about domain-invariant information and enhances the ability for generalization to new domains. DSP is designed to guide our model to focus on domain-specific knowledge using domain-related features. TOP is to capture task-oriented knowledge to generate high-quality summaries. Instead of fine-tuning the whole pre-trained language model (PLM), we only update the prompt networks but keep PLM fixed. Experimental results on the zero-shot setting show that the novel design of prompts can yield more coherent, faithful, and relevant summaries than baselines using the prefix-tuning, and perform at par with fine-tuning while being more efficient. Overall, our work introduces a prompt-based perspective to the zero-shot learning for dialogue summarization task and provides valuable findings and insights for future research. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3477495.3531933 | SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Keywords | DocType | Citations |
dialogue summarization, domain adaptation, lightweight fine-tuning, an adversarial way, domain-invariant, domain-specific, zero-shot | Conference | 0 |
PageRank | References | Authors |
0.34 | 2 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lulu Zhao | 1 | 0 | 1.35 |
| Fujia Zheng | 2 | 0 | 1.35 |
| Weihao Zeng | 3 | 0 | 0.68 |
| Ke-Qing He | 4 | 428 | 63.80 |
| Ruotong Geng | 5 | 0 | 0.34 |
| Huixing Jiang | 6 | 0 | 1.69 |
| Wei Wu | 7 | 124 | 54.63 |
| Weiran Xu | 8 | 210 | 43.79 |