The MRL 2022 Shared Task on Multilingual Clause-level Morphology
Omer Goldman, Francesco Tinner, Hila Gonen, Benjamin Muller, Victoria Basmov, Shadrack Kirimi, Lydia Nishimwe, Benoît Sagot, Djamé Seddah, Reut Tsarfaty, Duygu Ataman
Abstract
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.- Anthology ID:
- 2022.mrl-1.14
- Volume:
- Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Venue:
- MRL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 134–146
- Language:
- URL:
- https://aclanthology.org/2022.mrl-1.14
- DOI:
- Cite (ACL):
- Omer Goldman, Francesco Tinner, Hila Gonen, Benjamin Muller, Victoria Basmov, Shadrack Kirimi, Lydia Nishimwe, Benoît Sagot, Djamé Seddah, Reut Tsarfaty, and Duygu Ataman. 2022. The MRL 2022 Shared Task on Multilingual Clause-level Morphology. In Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL), pages 134–146, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- The MRL 2022 Shared Task on Multilingual Clause-level Morphology (Goldman et al., MRL 2022)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2022.mrl-1.14.pdf