Coherence is a crucial aspect of evaluating text readability and can be
assessed through two primary factors when evaluating an essay in a scoring
scenario. The first factor is logical coherence, characterized by the
appropriate use of discourse connectives and the establishment of logical
relationships between sentences. The second factor is the appropriateness of
punctuation, as inappropriate punctuation can lead to confused sentence
structure. To address these concerns, we propose a coherence scoring model
consisting of a regression model with two feature extractors: a local coherence
discriminative model and a punctuation correction model. We employ
gradient-boosting regression trees as the regression model and impose
monotonicity constraints on the input features. The results show that our
proposed model better generalizes unseen data. The model achieved third place
in track 1 of NLPCC 2023 shared task 7. Additionally, we briefly introduce our
solution for the remaining tracks, which achieves second place for track 2 and
first place for both track 3 and track 4.

我们提出了一个一致性评分模型，它由两个特征提取器组成：本地一致性判别模型和标点修正模型。使用梯度提升回归树作为回归模型，并对输入特征施加单调性约束，结果表明我们的模型在未见数据上具有更好的泛化能力。该模型在 NLPCC 2023 的 7 号共享任务的第一项中获得第三名，此外，我们还对剩余的项提出了解决方案，在第二项中获得第二名，在第三项和第四项中获得第一名。