论文地址:Constituency Parsing with a Self-Attentive Encoder 代码地址:github 今天要介绍的这篇论文是成分句法分析领域目前的state-of-the-art,结果最高的几篇paper可以参见ruder在github整理的列表:github。下面就是成分句法分析目前排名: 摘要 本篇论文将之前A Minimal Span-Based Neural Constituency Parser这篇论文...
To address this limitation, we propose a gated self-attentive encoder (GSAE) for NMT that aims to directly capture the dependency relationship between any two words of source-side regardless of their distance. The proposed GSAE gains access to a wider context and ensures the better representation...
《Global-Locally Self-Attentive Encoder for Dialogue State Tracking》阅读笔记 论文链接:https://arxiv.org/pdf/1805.09655.pdf 代码链接(附有数据):https://github.com/salesforce/glad 注:代码用Python实现的,且整个工程代码量相对不大,所以非常容易懂。看完论文后,建议结合代码和数据跑一跑,能极大加深对论文...
论文地址:Constituency Parsing with a Self-Attentive Encoder 代码地址:github 今天要介绍的这篇论文是成分句法分析领域目前的state-of-the-art,结果最高的几篇paper可以参见ruder在github整理的列表:github。 下面就是成分句法分析目前排名: 摘要 本篇论文将之前A Minimal Span-Based Neural Constituency Parser这篇论...
论文地址:Constituency Parsing with a Self-Attentive Encoder 代码地址:github 今天要介绍的这篇论文是成分句法分析领域目前的state-of-the-art,结果最高的几篇paper可以参见ruder在github整理的列表:github。
Constituency Parsing with a Self-Attentive Encoder 论文地址:Constituency Parsing with a Self-Attentive Encoder 代码地址:github 今天要介绍的这篇论文是成分句法分析领域目前的state-of-the-art,结果最高的几篇paper可以参见ruder在github整理的列表:github。
《Global-Locally Self-Attentive Encoder for Dialogue State Tracking》阅读笔记 论文链接:https://arxiv.org/pdf/1805.09655.pdf 代码链接(附有数据):https://github.com/salesforce/glad 注:代码用Python实现的,且整个工程代码量相对不大,所以非常容易懂。看完论文后,建议结合代码和数据跑一跑,能极大加深对论文...
Global-locally self-attentive encoder. 考虑相对于特定插槽 对序列进行编码的过程。设 表示序列中的字数, 表示嵌入的维数, 表示与序列中的字相对应的字嵌入。 我们使用全局双向 产生 的全局编码 。 其中 是 状态的维度。 考虑到时隙 ,我们使用局部双向 ...
A high-accuracy parser with models for 11 languages, implemented in Python. Based onConstituency Parsing with a Self-Attentive Encoderfrom ACL 2018, with additional changes described inMultilingual Constituency Parsing with Self-Attention and Pre-Training. ...
对,就是这样的,可以说是强强联合,将目前的parser SOTA模型(biaffine parser based bilstm)的提取特征层(bilstm)替换成self-attention(Transformer的Encoder层)来提取特征。效果和用bilstm的效果几乎是一样的: LAS基本一样,但是这篇文章新颖的点在哪里呢?