In this paper we present a Dutch and English dataset that can serve as a gold standard for evaluating text normalization approaches. With the combination of text messages, message board posts and tweets, these datasets represent a variety of user generated content. All data was manually normalized to their standard form using newly-developed guidelines. We perform automatic lexical normalizatio...

In this paper, we present the approaches used by our NLP systems to automatically extract templates for example-based machine translation and pun generation. Our translation system is able to extract an average of 73.25% correct translation templates, resulting in a translation quality that has a low word error rate of 18% when the test document contains sentence patterns matching the training ...

This paper investigates the usefulness of automatic machine translation metrics when analyzing the impact of source reformulations on the quality of machinetranslated user generated content. We propose a novel framework to quickly identify rewriting rules which improve or degrade the quality of MT output, by trying to rely on automatic metrics rather than human judgments. We find that this appr...

Distinct properties of translated text have been the subject of research in linguistics for many year (Baker, 1993). In recent years computational methods have been developed to empirically verify the linguistic theories about translated text (Baroni and Bernardini, 2006). While many characteristics of translated text are more apparent in comparison to the original text, most of the prior resea...

In this paper, we study how humans perceive the use of images as an additional knowledge source to machine-translate usergenerated product listings in an e-commerce company. We conduct a human evaluation where we assess how a multi-modal neural machine translation (NMT) model compares to two text-only approaches: a conventional state-of-the-art attention-based NMT and a phrase-based statistical...