This paper presents the creation of initial bilingual corpora for thirteen
very low-resource languages of India, all from Northeast India. It also
presents the results of initial translation efforts in these languages. It
creates the first-ever parallel corpora for these languages and provides
initial benchmark neural machine translation results for these languages. We
intend to extend these corpora to include a large number of low-resource Indian
languages and integrate the effort with our prior work with African and
American-Indian languages to create corpora covering a large number of
languages from across the world.

该论文介绍了为印度东北地区的十三种资源非常匮乏的语言创建初始的双语语料库，同时提供了这些语言的初始翻译结果。该论文还创建了这些语言的首个平行语料库，并提供了关于这些语言的基准神经机器翻译结果。我们打算将这些语料库扩展到包括大量资源匮乏的印度语言，并结合我们之前在非洲和美洲印第安语言方面的工作，创建覆盖世界各地大量语言的语料库。

构建东北印度极低资源语言机器翻译平行语料的首次尝试

First Attempt at Building Parallel Corpora for Machine Translation of  Northeast India's Very Low-Resource Languages

Machine translation (MT) requires a wide range of linguistic capabilities,
which current end-to-end models are expected to learn implicitly by observing
aligned sentences in bilingual corpora. In this work, we ask: \emph{How well do
MT models learn coreference resolution from implicit signal?} To answer this
question, we develop an evaluation methodology that derives coreference
clusters from MT output and evaluates them without requiring annotations in the
target language. We further evaluate several prominent open-source and
commercial MT systems, translating from English to six target languages, and
compare them to state-of-the-art coreference resolvers on three challenging
benchmarks. Our results show that the monolingual resolvers greatly outperform
MT models. Motivated by this result, we experiment with different methods for
incorporating the output of coreference resolution models in MT, showing
improvement over strong baselines.

本文评估了机器翻译 (MT) 模型从隐含信号中学习共指消解的能力，并探究了如何将共指消解模型的输出整合到 MT 模型中以提高翻译质量。研究结果表明，单语共指消解模型的性能远远优于 MT 模型。

评估和改进机器翻译模型的指代消解能力

Evaluating and Improving the Coreference Capabilities of Machine Translation Models

A wide range of applications play an important role in the daily work of the
modern human translator. However, the computational tools designed to aid in
the process of translation only benefit translation from or to a small minority
of the 7,000 languages of the world, those that we may call "privileged
languages". As for those translators who work with the remaining languages, the
marginalized languages in the digital world, they cannot benefit from the tools
that are speeding up the production of translation in the privileged languages.
We may ask whether it is possible to bridge the gap between what is available
for these languages and for the marginalized ones. This paper proposes a
framework for computer-assisted translation into marginalized languages and its
implementation in a web application for Spanish-Guarani translation. The
proposed system is based on a new theory for phrase-level translation in
contexts where adequate bilingual corpora are not available: Translation by
Generalized Segments (referred to as Minimal Dependency Translation in previous
work).

本文提出了一种基于 “广义片段翻译” 理论的计算机辅助翻译框架，并通过其在西班牙语 - 瓜拉尼语翻译中实现的 Web 应用程序，为边缘化语言的计算机翻译架起了一座桥梁。