Towards Open-Domain Topic Classification
Hantian Ding Jinrui Yang Yuqian Deng Hongming Zhang Dan Roth
We introduce an open-domain topic classification system that accepts
user-defined taxonomy in real time. Users will be able to classify a text
snippet with respect to any candidate labels they want, and get instant
response from our web interface. To obtain such flexibility, we build the
backend model in a zero-shot way. By training on a new dataset constructed from
Wikipedia, our label-aware text classifier can effectively utilize implicit
knowledge in the pretrained language model to handle labels it has never seen
before. We evaluate our model across four datasets from various domains with
different label sets. Experiments show that the model significantly improves
over existing zero-shot baselines in open-domain scenarios, and performs
competitively with weakly-supervised models trained on in-domain data.