A current problem in NLP is massaging and processing low-resource languages which lack
useful training attributes such as supervised data, number of native speakers or experts, etc …

Findings of the WMT 2016 Bilingual Document Alignment Shared Task Page 1 Proceedings of
the First Conference on Machine Translation, Volume 2: Shared Task Papers, pages 554–563 …

Neural machine translation systems trained on low-resource languages produce sub-
optimal results due to the scarcity of large parallel datasets. To alleviate this problem …

Document alignment aims to identify pairs of documents in two distinct languages that are of
comparable content or translations of each other. Such aligned data can be used for a …

We present and evaluate an approach to document alignment meant for efficiency and
portability, as it relies on automatically extracted lexical translations and simple set-theoretic …

As explained in Chap. 1 and later developed in Chap. 6, Machine Translation (MT) engines
need to be trained with large numbers of parallel sentences or segments. The quantity and …

This section concerns applications of comparable corpora beyond pure machine translation.
It has been argued [,] that downstream applications such as cross-lingual document …

In a parallel corpus we know which document is a translation of what by design. If the link
between documents in different languages is not known, it needs to be established. In this …

One of the core goals of Natural Language Processing (NLP) is to develop computational
representations and methods to compare and contrast text meaning across languages. Such …

With the rapid digitization of information, large quantities of text-heavy data is being
constantly generated in many languages and across domains such as web documents …