The classification uses features of topic posteriors: which is the proportion of the tokens assigned to topic k in document d. The baselines include polylingual tree LDA (Hu et al., 2014, ptLDA) which encodes the dictionary as a tree prior (Andrzejewski et al., 2009), Multilingual Topic Anchoring (Yuan et al., 2018, MTAnchor), and Multilingual Cultural-common Topic Analysis (Shi et al., 2016, MCTA).
our MTM has higher F1 than other models in both intra-and cross-lingual evaluations, while discovering coherent topics and meaningful topic links.