NIPS Proceedingsβ

Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages

Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018) pre-proceedings

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Authors

Conference Event Type: Poster

Abstract

Multilingual topic models can reveal patterns in cross-lingual document collections. However, existing models lack speed and interactivity, which prevents adoption in everyday corpora exploration or quick moving situations (e.g., natural disasters, political instability). First, we propose a multilingual anchoring algorithm that builds an anchor-based topic model for documents in different languages. Then, we incorporate interactivity to develop MTAnchor (Multilingual Topic Anchors), a system that allows users to refine the topic model. We test our algorithms on labeled English, Chinese, and Sinhalese documents. Within minutes, our methods can produce interpretable topics that are useful for specific classification tasks.