Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains

• URL: http://arxiv.org/abs/2201.11153v1
• Date: Wed, 26 Jan 2022 19:27:32 GMT
• Title: Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains
• Authors: Alon Albalak, Sharon Levy, and William Yang Wang
• Abstract summary: We demonstrate a cross-lingual open-retrieval question answering system for the emergent domain of COVID-19. Our system adopts a corpus of scientific articles to ensure that retrieved documents are reliable. We show that a deep semantic retriever greatly benefits from training on our English-to-all data and significantly outperforms a BM25 baseline in the cross-lingual setting.
• Score: 67.99403521976058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains. However, low-resource settings such as new and emerging domains would especially benefit from reliable question answering systems. Furthermore, multilingual and cross-lingual resources in emergent domains are scarce, leading to few or no such systems. In this paper, we demonstrate a cross-lingual open-retrieval question answering system for the emergent domain of COVID-19. Our system adopts a corpus of scientific articles to ensure that retrieved documents are reliable. To address the scarcity of cross-lingual training data in emergent domains, we present a method utilizing automatic translation, alignment, and filtering to produce English-to-all datasets. We show that a deep semantic retriever greatly benefits from training on our English-to-all data and significantly outperforms a BM25 baseline in the cross-lingual setting. We illustrate the capabilities of our system with examples and release all code necessary to train and deploy such a system.

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