The article discusses COCOGEN, a tool for representing commonsense structures through code. It details how the model generates outputs, including node and edge correctness evaluations for tasks like PROSCRIPT. A human evaluation complements automated metrics, where annotators compare generated graphs from COCOGEN and DAVINCI for relevance and correctness. The findings reveal COCOGEN's outputs align well with human judgments, highlighting its effectiveness in commonsense reasoning tasks. The article also addresses the limitations of automated assessments, underscoring the importance of human evaluation in validating results.
The results of our human evaluation suggest a significant correlation between the outputs generated by COCOGEN and human judgments, affirming its relevance and correctness.
Our exhaustive evaluation for PROSCRIPT involves scoring the correctness of nodes and edges separately, addressing limitations in automated metrics for model output assessment.
To assess the quality of outputs from COCOGEN and DAVINCI, we employed a human evaluation where annotators compared graphs based on relevance and correctness.
Our methodology records the performance of COCOGEN using both automated standard metrics and human evaluations to ensure comprehensive assessment of generation quality.
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