Towards reinterpreting neural topic models via composite activations

• Main Authors: LIM, Jia Peng, LAUW, Hady Wirawan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: topic modeling; composite topics; empirical study; machine learning; neural networks; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/7610; https://ink.library.smu.edu.sg/context/sis_research/article/8613/viewcontent/emnlp22.pdf
• Institution: Singapore Management University

Most Neural Topic Models (NTM) use a variational auto-encoder framework producing K topics limited to the size of the encoder’s output. These topics are interpreted through the selection of the top activated words via the weights or reconstructed vector of the decoder that are directly connected to...