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

• URL: http://arxiv.org/abs/2109.12028v1
• Date: Fri, 24 Sep 2021 15:32:45 GMT
• Title: Investigating Post-pretraining Representation Alignment for Cross-Lingual Question Answering
• Authors: Fahim Faisal, Antonios Anastasopoulos
• Abstract summary: We investigate the capabilities of multilingually pre-trained language models on cross-lingual question answering systems. We find that explicitly aligning the representations across languages with a post-hoc fine-tuning step generally leads to improved performance.
• Score: 20.4489424966613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 pre-trained language models on cross-lingual QA. We find that explicitly aligning the representations across languages with a post-hoc fine-tuning 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. Code and dataset are publicly available here: https://github.com/ffaisal93/aligned_qa

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