Abstract
Implicit discourse relation recognition is an important sub-task in discourse parsing, which needs to infer the relation based on proper discourse comprehension. Recent studies on cognitive learning strategies have suggested that using mental imagery strategy will foster text comprehension, which could effectively improve the capability of learners’ reading. Therefore, we propose a novel Mental Imagery-driven Neural Networks (MINN) to enhance representation for implicit discourse relation recognition. It employs the multi-granularity imagery vectors generated by the arguments to capture the deeper semantic information of discourse at different scales. Specifically, we 1) encode the different granularities of arguments (i.e., phrases, sentences.) and generate the corresponding imagery vectors as mentally imagining images of text content; 2) fuse the argument representations and imagery vectors as sequence representations; 3) further adopt self-attention to mine the important interactions between the sequence representations to infer the discourse relations. Extensive experimental results on the Penn Discourse TreeBank (PDTB) show that our model achieves competitive results against several state-of-the-art systems.
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Acknowledgments
We thank the anonymous reviewers for their valuable feedback. Our work is supported by the National Natural Science Foundation of China (61976154), and the Tianjin Natural Science Foundation (18JCYBJC15500).
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Wang, J., He, R., Guo, F., Han, Y. (2020). Mental Imagery-Driven Neural Network to Enhance Representation for Implicit Discourse Relation Recognition. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12397. Springer, Cham. https://doi.org/10.1007/978-3-030-61616-8_56
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