Visual Story-Telling is the process of forming a multi-sentence story from a
set of images. Appropriately including visual variation and contextual
information captured inside the input images is one of the most challenging
aspects of visual storytelling. Consequently, stories developed from a set of
images often lack cohesiveness, relevance, and semantic relationship. In this
paper, we propose a novel Vision Transformer Based Model for describing a set
of images as a story. The proposed method extracts the distinct features of the
input images using a Vision Transformer (ViT). Firstly, input images are
divided into 16X16 patches and bundled into a linear projection of flattened
patches. The transformation from a single image to multiple image patches
captures the visual variety of the input visual patterns. These features are
used as input to a Bidirectional-LSTM which is part of the sequence encoder.
This captures the past and future image context of all image patches. Then, an
attention mechanism is implemented and used to increase the discriminatory
capacity of the data fed into the language model, i.e. a Mogrifier-LSTM. The
performance of our proposed model is evaluated using the Visual Story-Telling
dataset (VIST), and the results show that our model outperforms the current
state of the art models.

该研究提出了一种基于 Vision Transformer 的模型来描述图像集，借助双向 LSTM 和注意力机制来捕捉图像的上下文信息和语义关系，模型在 Visual Story-Telling 数据集上的表现明显超过了目前的最先进模型。

基于视觉 Transformer 的图像描述故事模型

Vision Transformer Based Model for Describing a Set of Images as a Story

Stock market prediction is one of the most attractive research topic since
the successful prediction on the market's future movement leads to significant
profit. Traditional short term stock market predictions are usually based on
the analysis of historical market data, such as stock prices, moving averages
or daily returns. However, financial news also contains useful information on
public companies and the market. Existing methods in finance literature exploit
sentiment signal features, which are limited by not considering factors such as
events and the news context. We address this issue by leveraging deep neural
models to extract rich semantic features from news text. In particular, a
Bidirectional-LSTM are used to encode the news text and capture the context
information, self attention mechanism are applied to distribute attention on
most relative words, news and days. In terms of predicting directional changes
in both Standard & Poor's 500 index and individual companies stock price, we
show that this technique is competitive with other state of the art approaches,
demonstrating the effectiveness of recent NLP technology advances for
computational finance.

使用深度神经模型从新闻文本中提取语义特征，应用自注意机制分配注意力，并预测标准普尔 500 指数和个人公司股价的方向性变化，表明该技术与其他最先进的方法相当竞争，证明了最新的自然语言处理技术对计算金融业的有效性。

基于注意力机制循环神经网络的金融新闻股票趋势预测

Leveraging Financial News for Stock Trend Prediction with  Attention-Based Recurrent Neural Network

We present a deep neural architecture that parses sentences into three
semantic dependency graph formalisms. By using efficient, nearly arc-factored
inference and a bidirectional-LSTM composed with a multi-layer perceptron, our
base system is able to significantly improve the state of the art for semantic
dependency parsing, without using hand-engineered features or syntax. We then
explore two multitask learning approaches---one that shares parameters across
formalisms, and one that uses higher-order structures to predict the graphs
jointly. We find that both approaches improve performance across formalisms on
average, achieving a new state of the art. Our code is open-source and
available at this https URL

本文介绍了一种基于深度学习的语义依存图形式分析系统，该系统采用效率高且几乎拓扑结构简单的推理方法，通过组合双向 - LSTM 和多层感知机，能够在不使用手工特征或语法的情况下显著提高语义依存分析的技术水平。作者还对多任务学习策略进行了研究，并获得了新的技术成果。