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Dialogue Act Recognition Using Visual Information

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Document Analysis and Recognition – ICDAR 2021 (ICDAR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12822))

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Abstract

Automatic dialogue management including dialogue act (DA) recognition is usually focused on dialogues in the audio signal. However, some dialogues are also available in a written form and their automatic analysis is also very important.

The main goal of this paper thus consists in the dialogue act recognition from printed documents. For visual DA recognition, we propose a novel deep model that combines two recurrent neural networks.

The approach is evaluated on a newly created dataset containing printed dialogues from the English VERBMOBIL corpus. We have shown that visual information does not have any positive impact on DA recognition using good quality images where the OCR result is excellent. We have also demonstrated that visual information can significantly improve the DA recognition score on low-quality images with erroneous OCR.

To the best of our knowledge, this is the first attempt focused on DA recognition from visual data.

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Notes

  1. 1.

    The noise is not artificial (i.e. we didn’t perform any image transformation), but we have created the noise by real usage of the scanner. We put a blank piece of paper in the scanner and we changed the scanning quality by different scanning options and the amount of light.

  2. 2.

    https://github.com/martinekj/image-da-recognition.

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Acknowledgements

This work has been partly supported from ERDF “Research and Development of Intelligent Components of Advanced Technologies for the Pilsen Metropolitan Area (InteCom)” (no.: CZ.02.1.01/0.0/0.0/17_048/0007267) and by Grant No. SGS-2019-018 Processing of heterogeneous data and its specialized applications. We would like to thank also Mr. Matěj Zeman for some implementation work.

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Correspondence to Jiří Martínek .

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Martínek, J., Král, P., Lenc, L. (2021). Dialogue Act Recognition Using Visual Information. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12822. Springer, Cham. https://doi.org/10.1007/978-3-030-86331-9_51

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  • DOI: https://doi.org/10.1007/978-3-030-86331-9_51

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