Conversational Image Editing: Incremental Intent Identification in a New Dialogue Task

Ramesh Manuvinakurike, Trung Bui, Walter Chang, Kallirroi Georgila

Abstract
We present “conversational image editing”, a novel real-world application domain combining dialogue, visual information, and the use of computer vision. We discuss the importance of dialogue incrementality in this task, and build various models for incremental intent identification based on deep learning and traditional classification algorithms. We show how our model based on convolutional neural networks outperforms models based on random forests, long short term memory networks, and conditional random fields. By training embeddings based on image-related dialogue corpora, we outperform pre-trained out-of-the-box embeddings, for intention identification tasks. Our experiments also provide evidence that incremental intent processing may be more efficient for the user and could save time in accomplishing tasks.
Anthology ID:
W18-5033
Volume:
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Kazunori Komatani, Diane Litman, Kai Yu, Alex Papangelis, Lawrence Cavedon, Mikio Nakano
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
284–295
Language:
URL:
https://aclanthology.org/W18-5033
DOI:
10.18653/v1/W18-5033
Bibkey:
Cite (ACL):
Ramesh Manuvinakurike, Trung Bui, Walter Chang, and Kallirroi Georgila. 2018. Conversational Image Editing: Incremental Intent Identification in a New Dialogue Task. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 284–295, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Conversational Image Editing: Incremental Intent Identification in a New Dialogue Task (Manuvinakurike et al., SIGDIAL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/W18-5033.pdf
Data
MS COCO, Visual Genome
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