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Training and development data for the WMT18 QE task. Test data will be published as a separate item. This shared task will build on its previous six editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, phrase-level and sentence-level estimation. All tasks make use of datasets produced from post-editions by professional translators. The datasets are domain-specific (IT and life sciences/pharma domains) and extend from those used previous years with more instances and more languages. One important addition is that this year we also include datasets with neural MT outputs. In addition to advancing the state of the art at all prediction levels, our specific goals are: To study the performance of quality estimation approaches on the output of neural MT systems. We will do so by providing datasets for two language language pairs where the same source segments are translated by both a statistical phrase-based and a neural MT system. To study the predictability of deleted words, i.e. words that are missing in the MT output. TO do so, for the first time we provide data annotated for such errors at training time. To study the effectiveness of explicitly assigned labels for phrases. We will do so by providing a dataset where each phrase in the output of a phrase-based statistical MT system was annotated by human translators. To study the effect of different language pairs. We will do so by providing datasets created in similar ways for four language language pairs. To investigate the utility of detailed information logged during post-editing. We will do so by providing post-editing time, keystrokes, and actual edits. Measure progress over years at all prediction levels. We will do so by using last year's test set for comparative experiments. In-house statistical and neural MT systems were built to produce translations for all tasks. MT system-dependent information can be made available under request. The data is publicly available but since it has been provided by our industry partners it is subject to specific terms and conditions. However, these have no practical implications on the use of this data for research purposes. Participants are allowed to explore any additional data and resources deemed relevant.

Czech-Slovak parallel corpus consisting of several freely available corpora (Acquis [1], Europarl [2], Official Journal of the European Union [3] and part of OPUS corpus [4] – EMEA, EUConst, KDE4 and PHP) and downloaded website of European Commission [5]. Corpus is published in both in plaintext format and with an automatic morphological annotation. References: [1] http://langtech.jrc.it/JRC-Acquis.html/ [2] http://www.statmt.org/europarl/ [3] http://apertium.eu/data [4] http://opus.lingfil.uu.se/ [5] http://ec.europa.eu/

Pregnant women and children of the closer family getting dressed in yellow before the ceremony called "cuci perut" begins. In this ceremony the belly of a women who will be giving birth for the first time is washed ritually; much indonesian spoken with children.

A Hindi corpus of texts downloaded mostly from news sites. Contains both the original raw texts and an extensively cleaned-up and tokenized version suitable for language modeling. 18M sentences, 308M tokens

References throughout the corpus refer to the original recordings archived under this node. Those named "Tb" are digitized reel-to-reel tapes, all remaining digitizations originate from actual cassette tapes. In some cases the included pdf-scans of the cassette-covers contain additional metadata.

Nevumba talks about her life, about the relationship of the Akie with the Maasai asked by the researcher. Nevumba uses Kisankare words.

This dataset contains a number of user product reviews which are publicly available on the website of an established Czech online shop with electronic devices. Each review consists of negative and positive aspects of the product. This setting pushes the customer to rate important characteristics. We have selected 2000 positive and negative segments from these reviews and manually tagged their targets. Additionally, we selected 200 of the longest reviews and annotated them in the same way. The targets were either aspects of the evaluated product or some general attributes (e.g. price, ease of use).