  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 loss. Specifically, MWR
first trains the model on the uniformly weighted source examples, and measures
the efficacy of the model on the target examples via a loss function. By
iteratively performing a (meta) gradient descent, high-order gradients are
propagated to the source examples. These gradients are then used to update the
weights of source examples, in a way that is relevant to the target
performance. As MWR is model-agnostic, it can be applied to any backbone neural
model. Extensive experiments are conducted with various backbone text matching
models, on four widely used datasets and two tasks. The results demonstrate
that our proposed approach significantly outperforms a number of existing
adaptation methods and effectively improves the cross-dataset and cross-task
adaptability of the neural text matching models in the few-shot setting.
