用片段选择方法实现超长文本分类，兼具高精度与可解释性
MIL-BERT: Classification of Arbitrarily Large Text with Performance and Explanatory Guarantees
- 基于多实例学习思想，从长文本中筛选关键片段进行分类
- 在近100万词长文本上训练成功，3个数据集达最新最佳性能
- 适合处理超长文本分类任务，尤其适用于弱标注数据场景
许多文本分类决策仅依赖于其中的局部片段即可做出。受多实例学习启发，我们提出一种神经网络训练算法，通过选择关键片段来完成文本分类。该方法具有良好的可扩展性，已在接近100万词的样本上成功训练。我们在7个数据集上评估，重点针对远超基础模型编码限制的长文本集合。在3个任务中取得当前最优结果：新闻媒体政治偏见识别、长篇故事中的触发警告检测、推文集合中作者人口特征推断。此外，利用弱标签文本袋（bags）训练的模型，能准确分类其中的小规模实例。除达到新基准外，该方法是少数能在这些数据集上表现优异的神经网络方法之一。
原文摘要 · Abstract (English)
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 collections. Furthermore, the model trained on weakly-labeled collections of text (bags) generalizes to accurately classify constituent, smaller instances. Besides a new state-of-the-art for these problems, this approach is one of the few neural methods to excel in these datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。