Single-dataset Experts for Multi-dataset Question Answering (2109.13880v1)

Abstract: Many datasets have been created for training reading comprehension models, and a natural question is whether we can combine them to build models that (1) perform better on all of the training datasets and (2) generalize and transfer better to new datasets. Prior work has addressed this goal by training one network simultaneously on multiple datasets, which works well on average but is prone to over- or under-fitting different sub-distributions and might transfer worse compared to source models with more overlap with the target dataset. Our approach is to model multi-dataset question answering with a collection of single-dataset experts, by training a collection of lightweight, dataset-specific adapter modules (Houlsby et al., 2019) that share an underlying Transformer model. We find that these Multi-Adapter Dataset Experts (MADE) outperform all our baselines in terms of in-distribution accuracy, and simple methods based on parameter-averaging lead to better zero-shot generalization and few-shot transfer performance, offering a strong and versatile starting point for building new reading comprehension systems.

• The paper presents a novel MADE framework that improves multi-dataset question answering by integrating dataset-specific adapters with a shared Transformer.
• It demonstrates that a two-phase optimization process—joint training followed by individual adapter fine-tuning—yields superior in-domain accuracy and effective transfer learning.
• The framework consistently outperforms baseline models, highlighting its potential as a scalable solution for advanced reading comprehension systems and other NLP tasks.

Insightful Overview of "Single-dataset Experts for Multi-dataset Question Answering"

The paper "Single-dataset Experts for Multi-dataset Question Answering" presents an innovative framework designed to enhance the performance of reading comprehension models across diverse datasets. The authors introduce a novel approach, referred to as Multi-Adapter Dataset Experts (MADE), which aims to improve both in-distribution accuracy and transfer capabilities to new datasets. This framework leverages the strengths of single-dataset training while addressing the limitations of conventional multi-dataset models.

The paper outlines the shortcomings associated with traditional multi-dataset question answering systems, which often involve training a single model on multiple datasets simultaneously. Such practices can lead to potential overfitting or underfitting of the model on certain sub-distributions, thus impacting generalization abilities. MADE circumvents these issues by adopting a modular design, where the focus is on dataset-specific adapter modules integrated within a shared underlying Transformer model.

This configuration allows the adapters to capture dataset-specific nuances while the Transformer encodes cross-dataset regularities. The efficacy of this method is reflected in strong numerical results, where MADE consistently outperforms baseline models in terms of both in-domain performance and the capacity to generalize in zero-shot and few-shot transfer learning setups. Specifically, the MADE framework achieves higher F1 scores compared to both individual dataset-trained models and conventional multi-dataset-trained models across several established datasets.

Key findings in the paper include the superior performance of MADE following a two-phase optimization process. Initially, a joint optimization procedure trains both the shared Transformer and dataset-specific adapters across source datasets. Subsequently, individual adapters undergo fine-tuning, further enhancing model precision and robustness. Results demonstrate that this approach not only maximizes in-domain accuracy but also provides a robust foundation for transfer learning to new datasets.

Tables and figures in the paper substantiate these findings, highlighting how MADE performs better than the known UnifiedQA model, especially in environments with limited data availability. This positions MADE as a valuable multi-dataset model capable of serving as a benchmark for the development of future reading comprehension systems. Furthermore, the paper discusses the potential of adapting MADE to incorporate explicit mixture-modeling techniques to further refine zero-shot predictions and transfer learning.

The implications of this research are substantial both practically and theoretically. Practically, MADE offers a scalable solution for developing more versatile and accurate reading comprehension systems. On a theoretical level, it reinforces the significance of modular approaches in overcoming the limitations of traditional multi-dataset models, particularly in complex NLP tasks like question answering.

Speculating on future developments, the MADE framework could serve as a foundation for the creation of even more adaptable machine learning models capable of handling diverse data types and formats. Additionally, the adaptability and parameter efficiency of MADE suggest its potential application to other NLP tasks involving multiple datasets.

In conclusion, the paper presents a comprehensive and well-validated approach to improving multi-dataset question answering. MADE represents a significant step forward in developing reading comprehension models that are both robust and versatile, paving the way for enhanced understanding and engagement with text across varied contexts and domains.