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One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval
Akari Asai · Xinyan Yu · Jungo Kasai · Hanna Hajishirzi

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @

We present Cross-lingual Open-Retrieval Answer Generation (CORA), the first unified many-to-many question answering (QA) model that can answer questions across many languages, even for ones without language-specific annotated data or knowledge sources.We introduce a new dense passage retrieval algorithm that is trained to retrieve documents across languages for a question.Combined with a multilingual autoregressive generation model, CORA answers directly in the target language without any translation or in-language retrieval modules as used in prior work. We propose an iterative training method that automatically extends annotated data available only in high-resource languages to low-resource ones. Our results show that CORA substantially outperforms the previous state of the art on multilingual open QA benchmarks across 26 languages, 9 of which are unseen during training. Our analyses show the significance of cross-lingual retrieval and generation in many languages, particularly under low-resource settings.

Author Information

Akari Asai (University of Washington)
Xinyan Yu (Department of Computer Science, University of Washington)
Jungo Kasai (Paul G. Allen School of Computer Science & Engineering, University of Washington)
Hanna Hajishirzi (University of Washington)

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