Semantic textual similarity is the task of estimating the similarity between the meaning of two texts. In this paper, we fine-tune transformer architectures for semantic textual similarity on the Semantic Textual Similarity Benchmark by tuning the model partially and then end-to-end. We experiment with BERT, RoBERTa, and DeBERTaV3 cross-encoders by approaching the problem as a binary classification task or a regression task. We combine the outputs of the transformer models and use handmade features as inputs for boosting algorithms. Due to worse test set results coupled with improvements on the validation set, we experiment with different dataset splits to further investigate this occurrence. We also provide an error analysis, focused on the edges of the prediction range.

翻译：语义文本相似性是指估计两段文本在含义上相似程度的任务。本文通过在语义文本相似性基准上对Transformer架构进行部分调优，再实施端到端调整，以微调模型用于语义文本相似性任务。我们分别将问题视为二分类任务或回归任务，对BERT、RoBERTa和DeBERTaV3交叉编码器进行实验。我们将Transformer模型的输出进行组合，并利用手工构建的特征作为提升算法的输入。鉴于测试集结果较差而验证集性能有所提升的情况，我们尝试不同的数据集划分方式，以进一步探究此现象。此外，我们针对预测范围的边缘区域进行了误差分析。