Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension (1809.06963v3)

Abstract: We propose a multi-task learning framework to learn a joint Machine Reading Comprehension (MRC) model that can be applied to a wide range of MRC tasks in different domains. Inspired by recent ideas of data selection in machine translation, we develop a novel sample re-weighting scheme to assign sample-specific weights to the loss. Empirical study shows that our approach can be applied to many existing MRC models. Combined with contextual representations from pre-trained LLMs (such as ELMo), we achieve new state-of-the-art results on a set of MRC benchmark datasets. We release our code at https://github.com/xycforgithub/MultiTask-MRC.

• The paper introduces a multi-task learning framework with sample re-weighting that optimizes performance by leveraging auxiliary tasks across diverse datasets.
• It employs a novel sample-wise re-weighting algorithm to mitigate overfitting and enhance training specificity in machine reading comprehension models.
• Empirical evaluations demonstrate significant improvements in exact match and F1 scores on benchmarks such as SQuAD, NewsQA, and MS MARCO.

Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension: A Summary

The paper "Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension" by Xu et al. presents an innovative approach to enhance the performance of machine reading comprehension (MRC) models through multi-task learning (MTL). The research focuses on developing a unified model capable of handling a wide variety of MRC tasks across different domains, leveraging data from multiple sources to bolster generalization and accuracy.

Core Contributions and Methodology

The authors introduce a multi-task learning framework that integrates a sample re-weighting mechanism to optimize the training process of MRC models. This framework assigns variable weights to each training sample, inspired by data selection strategies in machine translation. The re-weighting approach seeks to mitigate overfitting concerns prevalent in MRC models trained on relatively small datasets. When used in conjunction with pre-trained contextual representations such as ELMo, this method attains state-of-the-art results on several MRC benchmarks.

1. Multi-task Learning Framework:
   • The MTL framework combines multiple MRC datasets in the training process, effectively utilizing auxiliary tasks to enhance performance on the target task. This strategy acts as an implicit data augmentation, leveraging diverse text domains to improve the MRC model's robustness and versatility.
2. Sample Re-weighting Scheme:
   • The paper introduces a novel sample re-weighting algorithm to adjust the importance of samples within the training loss. Compared to previous MTL techniques focusing on task-level weighting, this sample-wise granularity enhances the specificity and efficacy of the training process.
3. Performance Evaluation:
   • Empirical evaluations demonstrate that the proposed MTL framework combined with sample re-weighting significantly improves upon single-task baselines. The approach is validated using two state-of-the-art models—demonstrating notable performance enhancements on datasets such as SQuAD, NewsQA, and MS MARCO.

Key Results and Insights

The paper reports strong numerical outcomes, highlighting substantial performance gains attributable to the MTL approach. On NewsQA, the proposed model surpasses human baseline scores by achieving a 13.4-point improvement in exact match and a 3.2-point gain in F1 metrics. Furthermore, incorporating this MTL strategy with ELMo achieves leading results across evaluation metrics on several individual datasets. This signifies the framework's potential not only to improve existing benchmarks but also to synergize with contemporary LLMing methods for enhanced comprehension and generation capabilities.

Implications and Future Directions

The research presents several practical and theoretical implications. Practically, the MTL approach provides an efficient and scalable method for improving MRC systems using existing datasets without the need for extensive domain-specific tailoring. Theoretically, it opens avenues for further exploration into sample efficiency and generalization in neural models across multiple tasks.

Potential future developments may include extending this multi-task approach to larger pre-trained models such as BERT, to examine the interplay between massive pre-training and fine-tuned multitask training. Additionally, exploring the application of this framework to other problem domains within NLP, such as language inference or cross-lingual tasks, could broaden its applicability and validate its robustness in diverse contexts.

In conclusion, the multi-task learning approach with sample re-weighting as proposed in this paper marks a significant step in advancing the capabilities and generalizability of MRC frameworks, highlighting the potential of strategic data utilization in improving neural model performance across various domains.