论文标题
通过张量分解从非二元组成树学习
Learning from Non-Binary Constituency Trees via Tensor Decomposition
论文作者
论文摘要
以二进制形式处理句子选区树是文献中一种普遍且流行的方法。但是，组成树本质上是非二元的。二进制过程会深深地改变了结构，而促进成分相反。在这项工作中，我们介绍了一种新的方法来处理利用基于张量的模型的非二进制选区树。特别是，我们展示了基于规范张量分解的强大组成函数如何利用如此丰富的结构。我们方法的一个关键点是对因子矩阵施加的权重共享约束，这允许限制模型参数的数量。最后，我们引入了一个Tree-LSTM模型，该模型利用了此组成功能，并通过实验评估其在不同NLP任务上的性能。
Processing sentence constituency trees in binarised form is a common and popular approach in literature. However, constituency trees are non-binary by nature. The binarisation procedure changes deeply the structure, furthering constituents that instead are close. In this work, we introduce a new approach to deal with non-binary constituency trees which leverages tensor-based models. In particular, we show how a powerful composition function based on the canonical tensor decomposition can exploit such a rich structure. A key point of our approach is the weight sharing constraint imposed on the factor matrices, which allows limiting the number of model parameters. Finally, we introduce a Tree-LSTM model which takes advantage of this composition function and we experimentally assess its performance on different NLP tasks.
