Named entity recognition (NER) models are typically based on the architecture
of Bi-directional LSTM (BiLSTM). The constraints of sequential nature and the
modeling of single input prevent the full utilization of global information
from larger scope, not only in the entire sentence, but also in the entire
document (dataset). In this paper, we address these two deficiencies and
propose a model augmented with hierarchical contextualized representation:
sentence-level representation and document-level representation. In
sentence-level, we take different contributions of words in a single sentence
into consideration to enhance the sentence representation learned from an
independent BiLSTM via label embedding attention mechanism. In document-level,
the key-value memory network is adopted to record the document-aware
information for each unique word which is sensitive to similarity of context
information. Our two-level hierarchical contextualized representations are
fused with each input token embedding and corresponding hidden state of BiLSTM,
respectively. The experimental results on three benchmark NER datasets
(CoNLL-2003 and Ontonotes 5.0 English datasets, CoNLL-2002 Spanish dataset)
show that we establish new state-of-the-art results.

本文提出了一种基于分层上下文表示的模型，通过句子级和文档级表示来提高实体识别模型中全局信息的利用，其中包括标签嵌入和注意机制以及键值内存网络，实验证明该模型在三个基准数据集上表现出优越的效果。

命名实体识别的分层上下文表示

Hierarchical Contextualized Representation for Named Entity Recognition

This paper proposes a new framework to solve the problem of monocular visual
odometry, called MagicVO . Based on Convolutional Neural Network (CNN) and
Bi-directional LSTM (Bi-LSTM), MagicVO outputs a 6-DoF absolute-scale pose at
each position of the camera with a sequence of continuous monocular images as
input. It not only utilizes the outstanding performance of CNN in image feature
processing to extract the rich features of image frames fully but also learns
the geometric relationship from image sequences pre and post through Bi-LSTM to
get a more accurate prediction. A pipeline of the MagicVO is shown in Fig. 1.
The MagicVO system is end-to-end, and the results of experiments on the KITTI
dataset and the ETH-asl cla dataset show that MagicVO has a better performance
than traditional visual odometry (VO) systems in the accuracy of pose and the
generalization ability.

本文提出了 MagicVO 框架，基于卷积神经网络和双向 LSTM，对单眼视觉里程计问题进行求解，实验结果表明，MagicVO 在位姿准确性和泛化能力方面优于传统视觉里程计系统。

MagicVO: 通过深度双向递归卷积神经网络实现单目视觉里程计的端到端计算

MagicVO: End-to-End Monocular Visual Odometry through Deep  Bi-directional Recurrent Convolutional Neural Network

Previous machine comprehension (MC) datasets are either too small to train
end-to-end deep learning models, or not difficult enough to evaluate the
ability of current MC techniques. The newly released SQuAD dataset alleviates
these limitations, and gives us a chance to develop more realistic MC models.
Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM)
model, which is an end-to-end system that directly predicts the answer
beginning and ending points in a passage. Our model first adjusts each
word-embedding vector in the passage by multiplying a relevancy weight computed
against the question. Then, we encode the question and weighted passage by
using bi-directional LSTMs. For each point in the passage, our model matches
the context of this point against the encoded question from multiple
perspectives and produces a matching vector. Given those matched vectors, we
employ another bi-directional LSTM to aggregate all the information and predict
the beginning and ending points. Experimental result on the test set of SQuAD
shows that our model achieves a competitive result on the leaderboard.

在 SQuAD 数据集上，我们提出了一种多角度上下文匹配模型，它是一种端到端系统，可以直接预测文本段落中答案的起点和终点。我们的模型通过使用双向 LSTM 来编码问题和加权段落，利用多个角度将文本上下文与编码问题进行匹配，并生成一个匹配向量，最终利用另一个双向 LSTM 汇总所有信息并预测起点和终点。在 SQuAD 测试集上，实验结果显示我们的模型达到了领先位置的竞争力。