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Investigating Post-pretraining Representation Alignment for Cross-Lingual Question Answering

التحقيق في محاذاة التمثيل بعد الاحتجاج بها للنظرات الشاملة الإجابة

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 Publication date 2021
and research's language is English
 Created by Shamra Editor




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Human knowledge is collectively encoded in the roughly 6500 languages spoken around the world, but it is not distributed equally across languages. Hence, for information-seeking question answering (QA) systems to adequately serve speakers of all languages, they need to operate cross-lingually. In this work we investigate the capabilities of multilingually pretrained language models on cross-lingual QA. We find that explicitly aligning the representations across languages with a post-hoc finetuning step generally leads to improved performance. We additionally investigate the effect of data size as well as the language choice in this fine-tuning step, also releasing a dataset for evaluating cross-lingual QA systems.



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