Abstract
Multiple-choice question answering (MCQA) is one of the most challenging tasks in machine reading comprehension since it requires more advanced reading comprehension skills such as logical reasoning, summarization, and arithmetic operations. Unfortunately, most existing MCQA datasets are small in size, which increases the difficulty of model learning and generalization. To address this challenge, we propose a multi-source meta transfer (MMT) for low-resource MCQA. In this framework, we first extend meta learning by incorporating multiple training sources to learn a generalized feature representation across domains. To bridge the distribution gap between training sources and the target, we further introduce the meta transfer that can be integrated into the multi-source meta training. More importantly, the proposed MMT is independent of backbone language models. Extensive experiments demonstrate the superiority of MMT over state-of-the-arts, and continuous improvements can be achieved on different backbone networks on both supervised and unsupervised domain adaptation settings.
Highlights
There has been a growing interest in making machines to understand human languages, and a great progress has been made in machine reading comprehension (MRC)
The meta transfer learning (MTL) is the transfer learning module designed for multi-source meta learning (MML), and TL denotes the traditional transfer learning without MML
We observe that MML fine-tuned on MCTEST (MML(M)) is better than that on RACE (MML(R)), which is caused by the large difference between the RACE and DREAM datasets
Summary
There has been a growing interest in making machines to understand human languages, and a great progress has been made in machine reading comprehension (MRC). Different from extractive/abstractive QA whose answers are usually limited to the text spans exist in the passage, the answers of MCQA may not appear in the text passage and may involve complex Meta model Target MMT model 2. MCQA usually requires more advanced reading comprehension abilities, including arithmetic operation, summarization, logic reasoning and commonsense reasoning (Richardson et al, 2013; Sun et al, 2019a), and etc. The size of most existing MCQA datasets is much smaller than that of the extractive/abstractive QA datasets. The data size of most existing MCQA datasets are far less than 100k (see Table 1), and the smallest one only contains 660 samples
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