We introduce a new classification task for scientific statements and release
a large-scale dataset for supervised learning. Our resource is derived from a
machine-readable representation of the arXiv.org collection of preprint
articles. We explore fifty author-annotated categories and empirically motivate
a task design of grouping 10.5 million annotated paragraphs into thirteen
classes. We demonstrate that the task setup aligns with known success rates
from the state of the art, peaking at a 0.91 F1-score via a BiLSTM
encoder-decoder model. Additionally, we introduce a lexeme serialization for
mathematical formulas, and observe that context-aware models could improve when
also trained on the symbolic modality. Finally, we discuss the limitations of
both data and task design, and outline potential directions towards
increasingly complex models of scientific discourse, beyond isolated
statements.

本论文介绍了一个新的科学语句分类任务，并发布了一个大规模数据集用于监督学习。通过对 arXiv.org 的机器可读表现形式进行数据派生，我们探索了 50 个作者注释的类别，并通过将 1050 万个注释段分组为 13 类进行了任务设计，以证明任务设置与已知最新技术的成功率相一致。通过 BiLSTM 编码解码模型，演示了其 0.91F1 得分的高峰。此外，我们介绍了一种数学公式的词汇串行化，并观察到当上下文感知模型还在符号模态上训练时，可以进一步提高模型的性能。最后，本文讨论了数据和任务设计的限制，并概述了走向科学话语越来越复杂模型的潜在方向，超越了孤立的语句