TY - JOUR
T1 - An empirical study on crosslingual transfer in probabilistic topic models
AU - Hao, Shudong
AU - Paul, Michael J.
N1 - Publisher Copyright:
© 2020 Association for Computational Linguistics.
PY - 2020
Y1 - 2020
N2 - Probabilistic topic modeling is a common first step in crosslingual tasks to enable knowledge transfer and extract multilingual features. Although many multilingual topic models have been developed, their assumptions about the training corpus are quite varied, and it is not clear how well the different models can be utilized under various training conditions. In this article, the knowledge transfer mechanisms behind different multilingual topic models are systematically studied, and through a broad set of experiments with four models on ten languages, we provide empirical insights that can inform the selection and future development of multilingual topic models.
AB - Probabilistic topic modeling is a common first step in crosslingual tasks to enable knowledge transfer and extract multilingual features. Although many multilingual topic models have been developed, their assumptions about the training corpus are quite varied, and it is not clear how well the different models can be utilized under various training conditions. In this article, the knowledge transfer mechanisms behind different multilingual topic models are systematically studied, and through a broad set of experiments with four models on ten languages, we provide empirical insights that can inform the selection and future development of multilingual topic models.
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U2 - 10.1162/COLI_a_00369
DO - 10.1162/COLI_a_00369
M3 - Article
AN - SCOPUS:85083079490
SN - 0891-2017
VL - 46
SP - 95
EP - 134
JO - Computational Linguistics
JF - Computational Linguistics
IS - 1
ER -