Abstract
Many neural models have been built to carry out reading comprehension tasks. However, these models mainly focus on formal passages like news and book stories. Although human dialog is the most important part of daily life, machine reading comprehension on dialogs (i.e., passage completion on dialogs) has not been sufficiently explored. Existing models show some weaknesses when comprehending dialogs and they are unable to capture global information over a distance and local detailed information at the same time. This paper introduces a neural network model DeepDial that aims at addressing the problems mentioned above. The model explores both word-level and utterance-level information in a dialog, and achieves the state-of-the-art performance on the benchmark dataset constructed from a TV series Friends.
N. Hu and J. Zhou are equally contributed.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (61772036) and Key Laboratory of Science, Technology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology). We appreciate the anonymous reviewers for their helpful comments.
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Hu, N., Zhou, J., Wan, X. (2019). DeepDial: Passage Completion on Dialogs. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11641. Springer, Cham. https://doi.org/10.1007/978-3-030-26072-9_10
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