# Towards Open-Domain Topic Classification

arXiv: https://arxiv.org/abs/2306.17290 · HTML: https://mlsanity.com/paper/2306.17290/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 29 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Zero-shot User Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1809.00385/) (2018)
- [Integrating Semantic Knowledge to Tackle Zero-shot Text Classification](https://mlsanity.com/paper/1903.12626/) (2019)
- [Train Once, Test Anywhere: Zero-Shot Learning for Text Classification](https://mlsanity.com/paper/1712.05972/) (2017)
- [NatCat: Weakly Supervised Text Classification with Naturally Annotated Resources](https://mlsanity.com/paper/2009.14335/) (2020)

## Built on by (most cited first, 3 of 3)

- [WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categories](https://mlsanity.com/paper/2307.15293/) (2023)
- [Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification](https://mlsanity.com/paper/2309.13543/) (2023)
- [PaperRouter-Agent: A Content-Grounded LLM Agent for Personalized Hierarchical Paper Routing](https://mlsanity.com/paper/2607.11564/) (2026)

## Abstract (arXiv)

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.