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This paper reports on the first shared task on statistical parsing of morphologically rich languages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the evaluation metrics for parsing MRLs given(More)
Developing a full coreference system able to run all the way from raw text to semantic interpretation is a considerable engineering effort, yet there is very limited availability of off-the shelf tools for researchers whose interests are not in coreference, or for researchers who want to concentrate on a specific aspect of the problem. We present BART, a(More)
We describe the design, the evaluation setup, and the results of the DiscoMT 2015 shared task, which included two sub-tasks, relevant to both the machine translation (MT) and the discourse communities: (i) pronoun-focused translation, a practical MT task, and (ii) cross-lingual pronoun prediction, a classification task that requires no specific MT expertise(More)
BART (Versley et al., 2008) is a highly modular toolkit for coreference resolution that supports state-of-the-art statistical approaches and enables efficient feature engineering. For the SemEval task 1 on Coreference Resolution , BART runs have been submitted for Ger-man, English, and Italian. BART relies on a maximum entropy-based classifier for pairs of(More)
We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provide predictions on what pronoun class label should replace a placeholder value in the target-language text, provided in lemma-tised and PoS-tagged form. We(More)
We present work on tagging German discourse connectives using English training data and a German-English parallel corpus, and report first results towards a more comprehensive approach of doing annotation projection for explicit discourse relations. Our results show that (i) an approach based on a dictionary of connec-tives currently has advantages over a(More)
The term Morphologically Rich Languages (MRLs) refers to languages in which significant information concerning syntactic units and relations is expressed at word-level. There is ample evidence that the application of readily available statistical parsing models to such languages is susceptible to serious performance degradation. The first workshop on(More)