Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training

Yifan Gao, Qingyu Yin, Zheng Li, Rui Meng, Tong Zhao, Bing Yin, Irwin King, Michael Lyu


Abstract
Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text. Despite its recent flourishing, keyphrase generation on non-English languages haven’t been vastly investigated. In this paper, we call attention to a new setting named multilingual keyphrase generation and we contribute two new datasets, EcommerceMKP and AcademicMKP, covering six languages. Technically, we propose a retrieval-augmented method for multilingual keyphrase generation to mitigate the data shortage problem in non-English languages. The retrieval-augmented model leverages keyphrase annotations in English datasets to facilitate generating keyphrases in low-resource languages. Given a non-English passage, a cross-lingual dense passage retrieval module finds relevant English passages. Then the associated English keyphrases serve as external knowledge for keyphrase generation in the current language. Moreover, we develop a retriever-generator iterative training algorithm to mine pseudo parallel passage pairs to strengthen the cross-lingual passage retriever. Comprehensive experiments and ablations show that the proposed approach outperforms all baselines.
Anthology ID:
2022.findings-naacl.92
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Venues:
Findings | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1233–1246
Language:
URL:
https://aclanthology.org/2022.findings-naacl.92
DOI:
10.18653/v1/2022.findings-naacl.92
Bibkey:
Cite (ACL):
Yifan Gao, Qingyu Yin, Zheng Li, Rui Meng, Tong Zhao, Bing Yin, Irwin King, and Michael Lyu. 2022. Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1233–1246, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training (Gao et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-naacl.92.pdf
Code
 yifan-gao/multilingual_keyphrase_generation
Data
KP20kMicrosoft Academic Graph