MIL-BERT: Classification of Arbitrarily Large Text with Performance and Explanatory Guarantees
Many text classification decisions are viable based on constituent excerpts alone. Taking inspiration from the field of multiple instance learning, we present an algorithm for training a neural network to classify text by selecting such excerpts. We show that our approach is also scalable with demonstrated learning against samples with nearly 1M tokens. We evaluate our methods on 7 datasets with emphasis on long-textual collections that far exceed the encoding limit of our base model. We present state-of-the-art results with this algorithm on 3 datasets: identification of political bias in news outlets, trigger warnings in long stories, and demographic characteristics of authors in tweet ...
| Authors | John Cadigan, Dayne Freitag, Eric Yeh |
|---|---|
| Primary category | cs.CL |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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