Adaptable Text Matching via Meta-Weight Regulator
Bo Zhang, Chen Zhang, Fang Ma, Dawei Song

TL;DR
This paper introduces a meta-learning approach called Meta-Weight Regulator (MWR) that enhances neural text matching models' adaptability across datasets and tasks, especially in few-shot scenarios, by dynamically weighting source data based on relevance.
Contribution
The paper proposes a model-agnostic meta-learning method that learns to assign relevance-based weights to source examples to improve cross-dataset and cross-task adaptation in text matching.
Findings
MWR significantly outperforms existing adaptation methods.
It improves cross-dataset and cross-task performance in few-shot settings.
Extensive experiments validate the effectiveness of the proposed approach.
Abstract
Neural text matching models have been used in a range of applications such as question answering and natural language inference, and have yielded a good performance. However, these neural models are of a limited adaptability, resulting in a decline in performance when encountering test examples from a different dataset or even a different task. The adaptability is particularly important in the few-shot setting: in many cases, there is only a limited amount of labeled data available for a target dataset or task, while we may have access to a richly labeled source dataset or task. However, adapting a model trained on the abundant source data to a few-shot target dataset or task is challenging. To tackle this challenge, we propose a Meta-Weight Regulator (MWR), which is a meta-learning approach that learns to assign weights to the source examples based on their relevance to the target…
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