基于张量分解的非二元组元树学习
Learning from Non-Binary Constituency Trees via Tensor Decomposition
摘要：
以二进制形式处理句子选区树是文献中常见和流行的方法。然而，选区树本质上是非二元的。二进制化过程深刻地改变了结构，进一步的成分，而不是接近。在这项工作中，我们介绍了一种新的方法来处理非二进制选区树，利用基于张量的模型。特别是，我们展示了如何一个强大的组合函数的基础上的典型张量分解可以利用这样一个丰富的结构。我们的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，我们介绍了一个利用这个组合函数的Tree-LSTM模型，并通过实验评估了它在不同NLP任务上的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
