%0 Conference Proceedings %T Debunking Rumors on Twitter with Tree Transformer %A Ma, Jing %A Gao, Wei %Y Scott, Donia %Y Bel, Nuria %Y Zong, Chengqing %S Proceedings of the 28th International Conference on Computational Linguistics %D 2020 %8 December %I International Committee on Computational Linguistics %C Barcelona, Spain (Online) %F ma-gao-2020-debunking %X Rumors are manufactured with no respect for accuracy, but can circulate quickly and widely by “word-of-post” through social media conversations. Conversation tree encodes important information indicative of the credibility of rumor. Existing conversation-based techniques for rumor detection either just strictly follow tree edges or treat all the posts fully-connected during feature learning. In this paper, we propose a novel detection model based on tree transformer to better utilize user interactions in the dialogue where post-level self-attention plays the key role for aggregating the intra-/inter-subtree stances. Experimental results on the TWITTER and PHEME datasets show that the proposed approach consistently improves rumor detection performance. %R 10.18653/v1/2020.coling-main.476 %U https://aclanthology.org/2020.coling-main.476 %U https://doi.org/10.18653/v1/2020.coling-main.476 %P 5455-5466