Promoting Generalized Cross-lingual Question Answering in Few-resource Scenarios via Self-knowledge Distillation

TL;DR

This paper introduces a novel self-knowledge distillation method with mAP@k coefficients to improve generalized cross-lingual question answering, especially in resource-limited and zero-shot scenarios, outperforming traditional fine-tuning methods.

Contribution

It presents a new self-distillation training strategy with dynamic knowledge regulation, enhancing cross-lingual QA transfer in low-resource settings.

Findings

Outperforms standard cross-entropy fine-tuning in cross-lingual QA tasks.

Achieves competitive results with machine-translated data in resource-constrained scenarios.

Provides comprehensive analysis and ablation studies validating the approach.

Abstract

Despite substantial progress in multilingual extractive Question Answering (QA), models with high and uniformly distributed performance across languages remain challenging, especially for languages with limited resources. We study cross-lingual transfer mainly focusing on the Generalized Cross-Lingual Transfer (G-XLT) task, where the question language differs from the context language - a challenge that has received limited attention thus far. Our approach seeks to enhance cross-lingual QA transfer using a high-performing multilingual model trained on a large-scale dataset, complemented by a few thousand aligned QA examples across languages. Our proposed strategy combines cross-lingual sampling and advanced self-distillation training in generations to tackle the previous challenge. Notably, we introduce the novel mAP@k coefficients to fine-tune self-knowledge distillation loss, dynamically regulating the teacher's model knowledge to perform a balanced and effective knowledge transfer. We extensively evaluate our approach to assess XLT and G-XLT capabilities in extractive QA. Results reveal that our self-knowledge distillation approach outperforms standard cross-entropy fine-tuning by a significant margin. Importantly, when compared to a strong baseline that leverages a sizeable volume of machine-translated data, our approach shows competitive results despite the considerable challenge of operating within resource-constrained settings, even in zero-shot scenarios. Beyond performance improvements, we offer valuable insights through comprehensive analyses and an ablation study, further substantiating the benefits and constraints of our approach. In essence, we propose a practical solution to improve cross-lingual QA transfer by leveraging a few data resources in an efficient way.