We introduce the Constructive Comments Corpus (C3), comprised of 12,000
annotated news comments, intended to help build new tools for online
communities to improve the quality of their discussions. We define constructive
comments as high-quality comments that make a contribution to the conversation.
We explain the crowd worker annotation scheme and define a taxonomy of
sub-characteristics of constructiveness. The quality of the annotation scheme
and the resulting dataset is evaluated using measurements of inter-annotator
agreement, expert assessment of a sample, and by the constructiveness
sub-characteristics, which we show provide a proxy for the general
constructiveness concept. We provide models for constructiveness trained on C3
using both feature-based and a variety of deep learning approaches and
demonstrate that these models capture general rather than topic- or
domain-specific characteristics of constructiveness, through domain adaptation
experiments. We examine the role that length plays in our models, as comment
length could be easily gamed if models depend heavily upon this feature. By
examining the errors made by each model and their distribution by length, we
show that the best performing models are less correlated with comment
length.The constructiveness corpus and our experiments pave the way for a
moderation tool focused on promoting comments that make a contribution, rather
than only filtering out undesirable content.

本文介绍了 C3（Constructive Comments Corpus）语料库，包含 12,000 条经过注释的新闻评论，旨在帮助构建新的在线社区工具，以提高其讨论质量。通过定义建设性评论为对会话有所贡献的高质量评论，并通过定义建设性子特征的分类法以及使用基于特征和多种深度学习方法的建设性模型，以及通过领域适应实验的方法等，评估了注释方案的质量和造成的数据集。最终，本文提出了一种促进具有贡献性评论的工具，而不仅仅是过滤不良内容。