Towards Multilingual Neural Question Answering

Ekaterina Loginova, Stalin Varanasi, Günter Neumann

Abstract: Cross-lingual and multilingual question answering is a critical part of a successful and accessible natural language interface. However, many current solutions are unsatisfactory. We believe that recent developments in deep learning approaches are likely to be efficient for question answering tasks spanning several languages. This work aims to discuss current achievements and remaining challenges. We outline requirements and suggestions for practical parallel data collection and describe existing methods and datasets. We also demonstrate that a simple translation of texts can be inadequate in case of Arabic, English and German languages (on InsuranceQA and SemEval datasets), and thus more sophisticated models are required. We hope that our findings will ignite interest in neural approaches to multilingual question answering.