Dropped Pronouns (DP) in which pronouns are frequently dropped in the source
language but should be retained in the target language are challenge in machine
translation. In response to this problem, we propose a semi-supervised approach
to recall possibly missing pronouns in the translation. Firstly, we build
training data for DP generation in which the DPs are automatically labelled
according to the alignment information from a parallel corpus. Secondly, we
build a deep learning-based DP generator for input sentences in decoding when
no corresponding references exist. More specifically, the generation is
two-phase: (1) DP position detection, which is modeled as a sequential
labelling task with recurrent neural networks; and (2) DP prediction, which
employs a multilayer perceptron with rich features. Finally, we integrate the
above outputs into our translation system to recall missing pronouns by both
extracting rules from the DP-labelled training data and translating the
DP-generated input sentences. Experimental results show that our approach
achieves a significant improvement of 1.58 BLEU points in translation
performance with 66% F-score for DP generation accuracy.

本文提出了一种半监督方法，通过建立自动生成标注数据的 Dropped Pronouns 识别模型和基于深度学习的 DP 生成模型在翻译中识别并补充可能遗漏的代词，实验结果表明，该方法在翻译性能上有了显著提升。

一种新的省略代词翻译方法

A Novel Approach to Dropped Pronoun Translation

We present a semi-supervised approach that localizes multiple unknown object
instances in long videos. We start with a handful of labeled boxes and
iteratively learn and label hundreds of thousands of object instances. We
propose criteria for reliable object detection and tracking for constraining
the semi-supervised learning process and minimizing semantic drift. Our
approach does not assume exhaustive labeling of each object instance in any
single frame, or any explicit annotation of negative data. Working in such a
generic setting allow us to tackle multiple object instances in video, many of
which are static. In contrast, existing approaches either do not consider
multiple object instances per video, or rely heavily on the motion of the
objects present. The experiments demonstrate the effectiveness of our approach
by evaluating the automatically labeled data on a variety of metrics like
quality, coverage (recall), diversity, and relevance to training an object
detector.

本文提出一种半监督的方法，用于在长视频中定位多个未知的物体实例，通过这种方法可以有效地进行对象检测和跟踪，同时实现对大量静态物体实例的识别。

观察和学习：从视频中半监督学习目标检测器

Watch and Learn: Semi-Supervised Learning of Object Detectors from  Videos

As the amount of online document increases, the demand for document
classification to aid the analysis and management of document is increasing.
Text is cheap, but information, in the form of knowing what classes a document
belongs to, is expensive. The main purpose of this paper is to explain the
expectation maximization technique of data mining to classify the document and
to learn how to improve the accuracy while using semi-supervised approach.
Expectation maximization algorithm is applied with both supervised and
semi-supervised approach. It is found that semi-supervised approach is more
accurate and effective. The main advantage of semi supervised approach is
"Dynamically Generation of New Class". The algorithm first trains a classifier
using the labeled document and probabilistically classifies the unlabeled
documents. The car dataset for the evaluation purpose is collected from UCI
repository dataset in which some changes have been done from our side.

该研究论文讨论了使用期望最大化技术来分类文档的方法，利用半监督学习方法可以提高分类准确性，其主要优点是可以动态生成新分类。该论文使用了汽车数据集进行了实验验证。