论文标题
语义驱动的句子融合：建模和评估
Semantically Driven Sentence Fusion: Modeling and Evaluation
论文作者
论文摘要
句子融合是将相关句子加入连贯文本的任务。目前针对此任务的培训和评估方案基于单个参考基础真相，并且不考虑有效的融合变体。我们表明，这阻碍了模型从强大的捕获输入句子之间的语义关系。为了减轻这一点，我们提出了一种方法，即通过策划的等效词的结缔句，将基础真相解决方案自动扩展为多个参考。我们将此方法应用于大规模数据集，并将增强数据集用于模型培训和评估。为了使用多个参考来改善语义表示的学习，我们在多任务框架下使用辅助话语分类任务丰富了模型。我们的实验突出了我们对最先进模型的方法的改进。
Sentence fusion is the task of joining related sentences into coherent text. Current training and evaluation schemes for this task are based on single reference ground-truths and do not account for valid fusion variants. We show that this hinders models from robustly capturing the semantic relationship between input sentences. To alleviate this, we present an approach in which ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. We apply this method to a large-scale dataset and use the augmented dataset for both model training and evaluation. To improve the learning of semantic representation using multiple references, we enrich the model with auxiliary discourse classification tasks under a multi-tasking framework. Our experiments highlight the improvements of our approach over state-of-the-art models.
