Constituency parsing and nested named entity recognition (NER) are similar
tasks since they both aim to predict a collection of nested and non-crossing
spans. In this work, we cast nested NER to constituency parsing and propose a
novel pointing mechanism for bottom-up parsing to tackle both tasks. The key
idea is based on the observation that if we traverse a constituency tree in
post-order, i.e., visiting a parent after its children, then two consecutively
visited spans would share a boundary. Our model tracks the shared boundaries
and predicts the next boundary at each step by leveraging a pointer network. As
a result, it needs only linear steps to parse and thus is efficient. It also
maintains a parsing configuration for structural consistency, i.e., always
outputting valid trees. Experimentally, our model achieves the state-of-the-art
performance on PTB among all BERT-based models (96.01 F1 score) and competitive
performance on CTB7 in constituency parsing; and it also achieves strong
performance on three benchmark datasets of nested NER: ACE2004, ACE2005, and
GENIA. Our code is publicly available at
https://github.com/sustcsonglin/pointer-net-for-nested.

本文提出了一种新的指向机制，将嵌套的命名实体识别转换为成分分析，从而实现用线性步骤并保持结构一致性地解决两个相似任务的目的，并在多个基准测试数据集上取得了表现良好的成果。

使用指针网络实现自底向上的短语句法分析和嵌套命名实体识别

Bottom-Up Constituency Parsing and Nested Named Entity Recognition with  Pointer Networks

Image captioning has received significant attention with remarkable
improvements in recent advances. Nevertheless, images in the wild encapsulate
rich knowledge and cannot be sufficiently described with models built on
image-caption pairs containing only in-domain objects. In this paper, we
propose to address the problem by augmenting standard deep captioning
architectures with object learners. Specifically, we present Long Short-Term
Memory with Pointing (LSTM-P) --- a new architecture that facilitates
vocabulary expansion and produces novel objects via pointing mechanism.
Technically, object learners are initially pre-trained on available object
recognition data. Pointing in LSTM-P then balances the probability between
generating a word through LSTM and copying a word from the recognized objects
at each time step in decoder stage. Furthermore, our captioning encourages
global coverage of objects in the sentence. Extensive experiments are conducted
on both held-out COCO image captioning and ImageNet datasets for describing
novel objects, and superior results are reported when comparing to
state-of-the-art approaches. More remarkably, we obtain an average of 60.9% in
F1 score on held-out COCO~dataset.

提出了一种新的深度图片描述模型 Long Short-Term Memory with Pointing (LSTM-P)，结合对象学习方法和指向机制实现了词汇的扩充和全局物体涵盖。在 COCO 数据集和 ImageNet 数据集上的实验结果显示其优于现有方法，最终在 COCO 数据集上获得了平均 F1 分数为 60.9% 的表现