论文标题
多任务指针网络用于多代表解析
Multitask Pointer Network for Multi-Representational Parsing
论文作者
论文摘要
我们提出了一种基于过渡的方法，该方法通过训练单个模型可以有效解析具有组成和依赖树的任何输入句子，从而支持连续/射击和不连续/非注射/非注射性语法结构。为此，我们开发了一个指针网络体系结构，该体系结构具有两个独立的特定任务解码器和一个共同的编码器，并遵循多任务学习策略以共同训练它们。 The resulting quadratic system, not only becomes the first parser that can jointly produce both unrestricted constituent and dependency trees from a single model, but also proves that both syntactic formalisms can benefit from each other during training, achieving state-of-the-art accuracies in several widely-used benchmarks such as the continuous English and Chinese Penn Treebanks, as well as the discontinuous German NEGRA and TIGER datasets.
We propose a transition-based approach that, by training a single model, can efficiently parse any input sentence with both constituent and dependency trees, supporting both continuous/projective and discontinuous/non-projective syntactic structures. To that end, we develop a Pointer Network architecture with two separate task-specific decoders and a common encoder, and follow a multitask learning strategy to jointly train them. The resulting quadratic system, not only becomes the first parser that can jointly produce both unrestricted constituent and dependency trees from a single model, but also proves that both syntactic formalisms can benefit from each other during training, achieving state-of-the-art accuracies in several widely-used benchmarks such as the continuous English and Chinese Penn Treebanks, as well as the discontinuous German NEGRA and TIGER datasets.
