MODE: a multimodal open-domain dialogue dataset with explanation

Author:

Yin Hang,Lu Pinren,Li Ziang,Sun Bin,Li KanORCID

Publisher

Springer Science and Business Media LLC

Reference44 articles.

1. Anderson P, He X, Buehler C et al (2018) Bottom-up and top-down attention for image captioning and visual question answering. In: 2018 IEEE Conference on computer vision and pattern recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pp 6077–6086. https://doi.org/10.1109/CVPR.2018.00636. http://openaccess.thecvf.com/content_cvpr_2018/html/Anderson_Bottom-Up_and_Top-Down_CVPR_2018_paper.html

2. Baheti A, Sap M, Ritter A et al (2021) Just say no: Analyzing the stance of neural dialogue generation in offensive contexts. In: Moens M, Huang X, Specia L, et al (eds) Proceedings of the 2021 conference on empirical methods in natural language processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, pp 4846–4862, https://doi.org/10.18653/v1/2021.emnlp-main.397. https://doi.org/10.18653/v1/2021.emnlp-main.397

3. Boratko M, Li X, O’Gorman T et al (2020) Protoqa: A question answering dataset for prototypical common-sense reasoning. In: Webber B, Cohn T, He Y, et al (eds) Proceedings of the 2020 conference on empirical methods in natural language processing, EMNLP 2020, Online, November 16-20, 2020, pp 1122–1136. https://doi.org/10.18653/v1/2020.emnlp-main.85, https://doi.org/10.18653/v1/2020.emnlp-main.85

4. Budzianowski P, Wen T, Tseng B et al (2018) Multiwoz - A large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling. In: Riloff E, Chiang D, Hockenmaier J et al (eds) Proceedings of the 2018 conference on empirical methods in natural language processing, Brussels, Belgium, October 31 - November 4, 2018, pp 5016–5026. https://aclanthology.org/D18-1547/

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