Liu, Ziwen; Grau-Bove, Josep; Orr, Scott; (2022) BERT-Flow-VAE: A Weakly-supervised Model for Multi-Label Text Classification. In: (Proceedings) COLING2022: The 29th International Conference on Computational Linguistics. : Gyeongju, Republic of Korea.

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Abstract

Multi-label Text Classification (MLTC) is the task of categorizing documents into one or more topics. Considering the large volumes of data and varying domains of such tasks, fully-supervised learning requires manually fully annotated datasets which is costly and time-consuming. In this paper, we propose BERT-Flow-VAE (BFV), a WeaklySupervised Multi-Label Text Classification (WSMLTC) model that reduces the need for full supervision. This new model: (1) produces BERT sentence embeddings and calibrates them using a flow model, (2) generates an initial topic-document matrix by averaging results of a seeded sparse topic model and a textual entailment model that only require surface name of topics and 4-6 seed words per topic, and (3) adopts a VAE framework to reconstruct the embeddings under the guidance of the topic-document matrix. Finally, (4) it uses the means produced by the encoder model in the VAE architecture as predictions for MLTC. Experimental results on 6 multilabel datasets show that BFV can substantially outperform other baseline WSMLTC models in key metrics and achieve approximately 84% performance of a fully-supervised model.

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