Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages
Michelle Yuan, Benjamin Van Durme, Jordan L Ying
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.
BibTeX
@inproceedings{NEURIPS2018_28b9f8aa,
author = {Yuan, Michelle and Van Durme, Benjamin and Ying, Jordan L},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/28b9f8aa9f07db88404721af4a5b6c11-Paper.pdf},
volume = {31},
year = {2018}
}