论文标题
事实感知的句子分裂并通过排列不变培训重新塑造
Fact-aware Sentence Split and Rephrase with Permutation Invariant Training
论文作者
论文摘要
句子分裂和重新塑造旨在将复杂的句子分解为几个简单句子，其含义保留了。先前的研究倾向于通过SEQ2SEQ从并行句子对学习，该问题将复杂的句子作为输入，并顺序生成一系列简单的句子。但是，常规的SEQ2SEQ学习对此任务有两个限制：（1）它没有考虑到长期句子中所述的事实；结果，生成的简单句子可能会错过或不准确地陈述原始句子中的事实。 （2）要生成的简单句子的顺序差异可能会在训练过程中混淆SEQ2SEQ模型，因为从长源句子中得出的简单句子可以按任何顺序进行。 为了克服挑战，我们首先提出了事实感知的句子编码，这使该模型能够从长句子中学习事实，从而提高了句子的精确度；然后，我们引入置换不变的培训，以减轻SEQ2SEQ学习对此任务的订单差异的影响。 WebPlit-V1.0基准数据集上的实验表明，我们的方法可以在很大程度上改善先前SEQ2SEQ学习方法的性能。此外，对OIE基准测试的外在评估通过观察到，以我们最先进的模型将长期句子分解为预处理有助于提高开放性能，从而验证了我们方法的有效性。
Sentence Split and Rephrase aims to break down a complex sentence into several simple sentences with its meaning preserved. Previous studies tend to address the issue by seq2seq learning from parallel sentence pairs, which takes a complex sentence as input and sequentially generates a series of simple sentences. However, the conventional seq2seq learning has two limitations for this task: (1) it does not take into account the facts stated in the long sentence; As a result, the generated simple sentences may miss or inaccurately state the facts in the original sentence. (2) The order variance of the simple sentences to be generated may confuse the seq2seq model during training because the simple sentences derived from the long source sentence could be in any order. To overcome the challenges, we first propose the Fact-aware Sentence Encoding, which enables the model to learn facts from the long sentence and thus improves the precision of sentence split; then we introduce Permutation Invariant Training to alleviate the effects of order variance in seq2seq learning for this task. Experiments on the WebSplit-v1.0 benchmark dataset show that our approaches can largely improve the performance over the previous seq2seq learning approaches. Moreover, an extrinsic evaluation on oie-benchmark verifies the effectiveness of our approaches by an observation that splitting long sentences with our state-of-the-art model as preprocessing is helpful for improving OpenIE performance.
