NAACL 2022findings18 citations

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.

BibTeX
@inproceedings{gao-etal-2022-retrieval,
    title = "Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training",
    author = "Gao, Yifan  and
      Yin, Qingyu  and
      Li, Zheng  and
      Meng, Rui  and
      Zhao, Tong  and
      Yin, Bing  and
      King, Irwin  and
      Lyu, Michael",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-naacl.92/",
    doi = "10.18653/v1/2022.findings-naacl.92",
    pages = "1233--1246"
}
Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training · NAACL 2022