Multilingual Answer Sentence Reranking via Automatically Translated Data

Understand

We present a study on the design of multilingual Answer Sentence Selection (AS2) models, which are a core component of modern Question Answering (QA) systems.

• The main idea is to transfer data, created from one resource rich language, e.g., English, to other languages, less rich in terms of resources.
• The main findings of this paper are: (i) the training data for AS2 translated into a target language can be used to effectively fine-tune a Transformer-based model for that language; (ii) one multilingual Transformer model it is enough to rank answers in multiple languages; and (iii) mixed-language question/answer pairs can be used to fine-tune models to select answers from any language, where the input question is just in one language.
• This highly reduces the complexity and technical requirement of a multilingual QA system.