In this paper, we describe a parallel corpus annotated with full coreference chains that has been created to address an important problem that machine translation and other multilingual natural language processing (NLP) technologies face – translation of coreference across languages. Recent research in multilingual coreference and automatic pronoun translation has led to important insights into the problem and some promising results. However, its scope has been restricted to pronouns, whereas the phenomenon is not limited to anaphoric pronouns. Our corpus contains parallel texts for the language pair English-German, two major European languages. Despite being typologically very close, these languages still have systemic differences in the realisation of coreference, and thus pose problems for multilingual coreference resolution and machine translation. Our parallel corpus with full annotation of coreference will be a valuable resource with a variety of uses not only for NLP applications, but also for contrastive linguists and researchers in translation studies. This resource supports research on the mechanisms involved in coreference translation in order to develop a better understanding of the phenomenon. The corpus is available from the LINDAT repository at http://hdl.handle.net/11372/LRT-2614.
@InProceedings{LAPSHINOVA-KOLTUNSKI18.941, author = {Ekaterina Lapshinova-Koltunski and Christian Hardmeier and Pauline Krielke}, title = "{ParCorFull: a Parallel Corpus Annotated with Full Coreference}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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