A number of cross-lingual transfer learning approaches based on neural
networks have been proposed for the case when large amounts of parallel text
are at our disposal. However, in many real-world settings, the size of parallel
annotated training data is restricted. Additionally, prior cross-lingual
mapping research has mainly focused on the word level. This raises the question
of whether such techniques can also be applied to effortlessly obtain
cross-lingually aligned sentence representations. To this end, we propose an
Adversarial Bi-directional Sentence Embedding Mapping (ABSent) framework, which
learns mappings of cross-lingual sentence representations from limited
quantities of parallel data.

提出了 Adversarial Bi-directional Sentence Embedding Mapping（ABSent）框架，它能够从有限量的平行数据中学习跨语言句子表示的映射，解决了跨语言转移学习中平行数据量有限的问题。

ABsent: 双向 GAN 的跨语言句子表示映射

ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional  GANs

Parallel text is required for building high-quality machine translation (MT)
systems, as well as for other multilingual NLP applications. For many South
Asian languages, such data is in short supply. In this paper, we described a
new publicly available corpus (PMIndia) consisting of parallel sentences which
pair 13 major languages of India with English. The corpus includes up to 56000
sentences for each language pair. We explain how the corpus was constructed,
including an assessment of two different automatic sentence alignment methods,
and present some initial NMT results on the corpus.

本研究提供了一个新的公开平行语料库（PMIndia），其中包含 13 种主要印度语言和英语的成对平行句子，每种语言对包含高达 56000 个句子。该语料库的构建及两种不同自动句子对齐方法的评估被描述，同时还介绍了该语料库在 NMT 方面的一些初步结果。

PMIndia -- 印度语言平行语料库集合

PMIndia -- A Collection of Parallel Corpora of Languages of India

This annotation style guide was created by and for the Blinker project at the
University of Pennsylvania. The Blinker project was so named after the
``bilingual linker'' GUI, which was created to enable bilingual annotators to
``link'' word tokens that are mutual translations in parallel texts. The
parallel text chosen for this project was the Bible, because it is probably the
easiest text to obtain in electronic form in multiple languages. The languages
involved were English and French, because, of the languages with which the
project co-ordinator was familiar, these were the two for which a sufficient
number of annotators was likely to be found.

本文介绍了 Blinker 项目团队在 Bible 平行语料库上对英法双语注释的样式指南。