TY - GEN
T1 - The MRL 2022 Shared Task on Multilingual Clause-level Morphology
AU - Goldman, Omer
AU - Tinner, Francesco
AU - Gonen, Hila
AU - Muller, Benjamin
AU - Basmov, Victoria
AU - Kirimi, Shadrack
AU - Nishimwe, Lydia
AU - Sagot, Benoît
AU - Seddah, Djamé
AU - Tsarfaty, Reut
AU - Ataman, Duygu
N1 - Publisher Copyright: © 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - The 2022 Multilingual Representation Learning (MRL) Shared Task was dedicated to clause-level morphology. As the first ever benchmark that defines and evaluates morphology outside its traditional lexical boundaries, the shared task on multilingual clause-level morphology sets the scene for competition across different approaches to morphological modeling, with 3 clause-level sub-tasks: morphological inflection, reinflection and analysis, where systems are required to generate, manipulate or analyze simple sentences centered around a single content lexeme and a set of morphological features characterizing its syntactic clause. This year's tasks covered eight typologically distinct languages: English, French, German, Hebrew, Russian, Spanish, Swahili and Turkish. The tasks has received submissions of four systems from three teams which were compared to two baselines implementing prominent multilingual learning methods. The results show that modern NLP models are effective in solving morphological tasks even at the clause level. However, there is still room for improvement, especially in the task of morphological analysis.
AB - The 2022 Multilingual Representation Learning (MRL) Shared Task was dedicated to clause-level morphology. As the first ever benchmark that defines and evaluates morphology outside its traditional lexical boundaries, the shared task on multilingual clause-level morphology sets the scene for competition across different approaches to morphological modeling, with 3 clause-level sub-tasks: morphological inflection, reinflection and analysis, where systems are required to generate, manipulate or analyze simple sentences centered around a single content lexeme and a set of morphological features characterizing its syntactic clause. This year's tasks covered eight typologically distinct languages: English, French, German, Hebrew, Russian, Spanish, Swahili and Turkish. The tasks has received submissions of four systems from three teams which were compared to two baselines implementing prominent multilingual learning methods. The results show that modern NLP models are effective in solving morphological tasks even at the clause level. However, there is still room for improvement, especially in the task of morphological analysis.
UR - http://www.scopus.com/inward/record.url?scp=85154619876&partnerID=8YFLogxK
M3 - منشور من مؤتمر
T3 - MRL 2022 - 2nd Workshop on Multi-Lingual Representation Learning, Proceedings of the Workshop
SP - 134
EP - 146
BT - MRL 2022 - 2nd Workshop on Multi-Lingual Representation Learning, Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 2nd Workshop on Multi-Lingual Representation Learning, MRL 2022
Y2 - 8 December 2022
ER -