no code implementations • EMNLP (WNUT) 2020 • Yulia Grishina, Thomas Gueudre, Ralf Winkler
True-casing, the task of restoring proper case to (generally) lower case input, is important in downstream tasks and for screen display.
no code implementations • NAACL (ACL) 2022 • Yulia Grishina, Daniil Sorokin
First, we apply the original LGL schedule on our data and then adapt LGL to the production setting where the full data is not available at initial training iterations.
no code implementations • WS 2018 • Massimo Poesio, Yulia Grishina, Varada Kolhatkar, Nafise Moosavi, Ina Roesiger, Adam Roussel, Fabian Simonjetz, Alex Uma, ra, Olga Uryupina, Juntao Yu, Heike Zinsmeister
The most distinctive feature of the corpus is the annotation of a wide range of anaphoric relations, including bridging references and discourse deixis in addition to identity (coreference).
no code implementations • WS 2017 • Yulia Grishina
Although parallel coreference corpora can to a high degree support the development of SMT systems, there are no large-scale parallel datasets available due to the complexity of the annotation task and the variability in annotation schemes.
no code implementations • WS 2017 • Yulia Grishina, Manfred Stede
In this paper, we examine the possibility of using annotation projection from multiple sources for automatically obtaining coreference annotations in the target language.
no code implementations • WS 2017 • Yulia Grishina
We additionally offer a more traditional setting, targeting the development of a multilingual coreference resolver without any restrictions on the resources and methods used.