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

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, Ecom-merceMKP and AcademicMKP, covering six languages. Technically, we propose a retrievalaugmented method for multilingual keyphrase generation to mitigate the data shortage problem in non-English languages. The retrievalaugmented 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 retrievergenerator 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. 1 * Qingyu Yin and Zheng Li are corresponding authors. This work was mainly done while Rui Meng was a Ph.D. student at University of Pittsburgh.1 The datasets are released at https://github. com/Yifan-Gao/multilingual_keyphrase_ generation.