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 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.
Authors: Yifan Gao, Qingyu Yin, Zheng Li, Rui Meng, Tong Zhao, Bing Yin, Irwin King, Michael Rung-Tsong Lyu
Published in: Findings of the Association for Computational Linguistics: NAACL (2022)