Existing unsupervised video-to-video translation methods fail to produce
translated videos which are frame-wise realistic, semantic information
preserving and video-level consistent. In this work, we propose UVIT, a novel
unsupervised video-to-video translation model. Our model decomposes the style
and the content, uses the specialized encoder-decoder structure and propagates
the inter-frame information through bidirectional recurrent neural network
(RNN) units. The style-content decomposition mechanism enables us to achieve
style consistent video translation results as well as provides us with a good
interface for modality flexible translation. In addition, by changing the input
frames and style codes incorporated in our translation, we propose a video
interpolation loss, which captures temporal information within the sequence to
train our building blocks in a self-supervised manner. Our model can produce
photo-realistic, spatio-temporal consistent translated videos in a multimodal
way. Subjective and objective experimental results validate the superiority of
our model over existing methods. More details can be found on our project
website: this https URL

该研究提出了一个名为 UVIT 的新型无监督视频到视频翻译模型，采用专用编码器 - 解码器结构和双向循环神经网络单元，通过风格内容分解机制使得其能够实现样式一致的视频翻译结果，并通过更改输入帧和风格码提出一种视频内插损失，可以以自我监督的方式训练建筑块。该模型能够以多模式方式生成现实照片和时空一致性的翻译视频。

无监督多模态视频到视频的自监督学习翻译

Unsupervised Multimodal Video-to-Video Translation via Self-Supervised  Learning

Parallel sentence extraction is a task addressing the data sparsity problem
found in multilingual natural language processing applications. We propose a
bidirectional recurrent neural network based approach to extract parallel
sentences from collections of multilingual texts. Our experiments with noisy
parallel corpora show that we can achieve promising results against a
competitive baseline by removing the need of specific feature engineering or
additional external resources. To justify the utility of our approach, we
extract sentence pairs from Wikipedia articles to train machine translation
systems and show significant improvements in translation performance.

该研究提出了一种基于双向循环神经网络的并行句子抽取方法，旨在解决多语言自然语言处理应用中数据稀疏性的问题。通过从维基百科文章中提取句子对来训练机器翻译系统，实验证明采用该方法能够显著提高翻译性能。

利用双向递归神经网络提取平行句子以改善机器翻译

Extracting Parallel Sentences with Bidirectional Recurrent Neural  Networks to Improve Machine Translation

We present a sequential model for temporal relation classification between
intra-sentence events. The key observation is that the overall syntactic
structure and compositional meanings of the multi-word context between events
are important for distinguishing among fine-grained temporal relations.
Specifically, our approach first extracts a sequence of context words that
indicates the temporal relation between two events, which well align with the
dependency path between two event mentions. The context word sequence, together
with a parts-of-speech tag sequence and a dependency relation sequence that are
generated corresponding to the word sequence, are then provided as input to
bidirectional recurrent neural network (LSTM) models. The neural nets learn
compositional syntactic and semantic representations of contexts surrounding
the two events and predict the temporal relation between them. Evaluation of
the proposed approach on TimeBank corpus shows that sequential modeling is
capable of accurately recognizing temporal relations between events, which
outperforms a neural net model using various discrete features as input that
imitates previous feature based models.

本文提出了一种用于在句内事件之间进行时间关系分类的序列模型。我们的方法首先提取一个上下文词序列，该词序列与两个事件提及之间的依赖路径很好地对齐，并使用生成的词序列对应的词性标记序列和依赖关系序列作为输入，提供给双向循环神经网络 (LSTM) 模型。神经网络学习组成句法和语义上下文表征，以预测它们之间的时间关系。该方法在 TimeBank 语料库上的评估表明，顺序建模能够准确地识别事件之间的时间关系，并且胜过了以前基于特征的模型。