Lengthy documents pose a unique challenge to neural language models due to
substantial memory consumption. While existing state-of-the-art (SOTA) models
segment long texts into equal-length snippets (e.g., 128 tokens per snippet) or
deploy sparse attention networks, these methods have new challenges of context
fragmentation and generalizability due to sentence boundaries and varying text
lengths. For example, our empirical analysis has shown that SOTA models
consistently overfit one set of lengthy documents (e.g., 2000 tokens) while
performing worse on texts with other lengths (e.g., 1000 or 4000). In this
study, we propose a Length-Aware Multi-Kernel Transformer (LAMKIT) to address
the new challenges for the long document classification. LAMKIT encodes lengthy
documents by diverse transformer-based kernels for bridging context boundaries
and vectorizes text length by the kernels to promote model robustness over
varying document lengths. Experiments on five standard benchmarks from health
and law domains show LAMKIT outperforms SOTA models up to an absolute 10.9%
improvement. We conduct extensive ablation analyses to examine model robustness
and effectiveness over varying document lengths.

长篇文档分类方面的研究中，我们提出了一种适应长文档的多核变换器（LAMKIT），通过使用多样化的基于变换器的核心来编码长篇文档，以解决上下文边界和文档长度差异性带来的问题，并通过这些核心向量化文本长度以提高模型的稳健性。在健康和法律领域的五个标准测试中，LAMKIT 相对于现有技术提升了高达 10.9% 的绝对改进。我们进行了大量的消融分析，以检验模型在不同文档长度上的稳健性和有效性。