DTC: Transfer Learning for Commonsense Machine Comprehension
Neurocomputing(2020)
摘要
Commonsense Machine Comprehension (CMC) is a popular natural language understanding task. CMC enables computers to learn about causal and temporal reasoning by exploiting implicit commonsense knowledge and can be applied to Question Answering, Search Engine and Dialogue System. Previous methods for CMC limit the vision on CMC task, neglecting that Recognizing Textual Entailment(RTE) task has much similarities with CMC task. In this paper, we propose a transfer learning model, which can take advantage of commonsense knowledge in RTE task by mapping CMC examples and RTE examples to a shared feature space and comprehending in this feature space. Specifically, we first establish a transfer learning framework which has three components: (1) source and target mappings, (2) domain regularization, and (3) CMC score function. Then we make selection for each component in our transfer learning framework and propose the Domain Transfer Comprehension(DTC) model. Experiments on Story Cloze Test show that our model outperforms most previous approaches and provides competitive results with state-of-art methods. We also show each components of our model have positive effect on performance.
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关键词
Commonsense machine comprehension,Recognition textual entailment,Transfer learning,Deep learning
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