Improving the Generalization Ability in Essay Coherence Evaluation
  through Monotonic Constraints
Chen Zheng Huan Zhang Yan Zhao Yuxuan Lai
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.