DRAGNN：一个基于迁移的动态连接神经网络框架
DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks
摘要：
在这项工作中，我们提出了一个紧凑的，模块化的框架，用于构建新的递归神经架构。我们的基本模块是一个新的通用单元，基于转换的递归单元（TBRU）。除了隐藏层激活之外，TBRU还具有离散状态动态，允许根据中间激活动态地建立网络连接。通过连接多个TBRU，我们可以扩展和组合常用的体系结构，如序列到序列，注意力机制和递归树结构模型。TBRU还可以同时作为下游任务的编码器和自身任务的解码器，从而实现更准确的多任务学习。我们称我们的方法为动态递归非循环图形神经网络，或DRAGNN。我们发现，DRAGNN是显着更准确和更有效的比seq2seq注意语法依存句法分析，并产生更准确的提取摘要任务的多任务学习。
英文摘要：
In this work, we present a compact, modular framework for constructing novel recurrent neural architectures. Our basic module is a new generic unit, the Transition Based Recurrent Unit (TBRU). In addition to hidden layer activations, TBRUs have discrete state dynamics that allow network connections to be built dynamically as a function of intermediate activations. By connecting multiple TBRUs, we can extend and combine commonly used architectures such as sequence-to-sequence, attention mechanisms, and re-cursive tree-structured models. A TBRU can also serve as both an encoder for downstream tasks and as a decoder for its own task simultaneously, resulting in more accurate multi-task learning. We call our approach Dynamic Recurrent Acyclic Graphical Neural Networks, or DRAGNN. We show that DRAGNN is significantly more accurate and efficient than seq2seq with attention for syntactic dependency parsing and yields more accurate multi-task learning for extractive summarization 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。
