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.

本文提出一种基于多重参考的句子融合方法，通过使用连接短语的等价类将基准解扩展到多个参考，结合多任务框架来增强模型的语义表示学习以及进行模型训练和评估，实验结果表明该方法优于现有模型。