Cross-Lingual GENQA: A Language-Agnostic Generative Question Answering Approach for Open-Domain Question Answering

Abstract

Open-Retrieval Generative Question Answering (GENQA) is proven to deliver high-quality, natural-sounding answers in English. In this paper, we present the first generalization of the GENQA approach for the multilingual environment. To this end, we present the GEN-TYDIQA dataset, which extends the TyDiQA evaluation data (Clark et al., 2020) with natural-sounding, well-formed answers in Arabic, Bengali, English, Japanese, and Russian. For all these languages, we show that a GENQA sequence-to-sequence-based model outperforms a state-of-the-art Answer Sentence Selection model. We also show that a multilingually-trained model competes with, and in some cases outperforms, its monolingual counterparts. Finally, we show that our system can even compete with strong baselines, even when fed with information from a variety of languages. Essentially, our system is able to answer a question in any language of our language set using information from many languages, making it the first LanguageAgnostic GENQA system