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We present a new treebank of English and French technical forum content which has been annotated for grammatical errors and phrase structure. This double annotation allows us to empirically measure the effect of errors on parsing performance. While it is slightly easier to parse the corrected versions of the forum sentences, the errors are not the main factor in making this kind of text hard to parse.

Supplementary data selection is a strongly motivated approach in domain adaptation of statistical machine translation systems. In this paper we report a novel approach of data selection guided by automatic quality estimation. In contrast to the conventional approach of using the entire target-domain data as reference for data selection, we restrict the reference set only to sentences poorly translated by the baseline model. Automatic quality estimation is used to identify such poorly translated sentences in the target domain. Our experiments reveal that this approach provides statistically significant improvements over the unadapted baseline and achieves comparable scores to that of conventional data selection approaches with significantly smaller amounts of selected data.

With the development of Web 2.0, a lot of content is nowadays generated online by users. Due to its characteristics (e.g., use of jargon and abbreviations, typos, grammatical and style errors), the user-generated content poses specific challenges to machine translation. This paper presents an online platform devoted to the pre-editing of user-generated content and its post-editing, two main types of human assistance strategies which are combined with domain adaptation and other techniques in order to improve the translation of this type of content. The platform has recently been released publicly and is being tested by two main types of user communities, namely, technical forum users and volunteer translators.

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