Character-Level Models versus Morphology in Semantic Role Labeling

Gözde Gül Şahin, Mark Steedman


Abstract
Character-level models have become a popular approach specially for their accessibility and ability to handle unseen data. However, little is known on their ability to reveal the underlying morphological structure of a word, which is a crucial skill for high-level semantic analysis tasks, such as semantic role labeling (SRL). In this work, we train various types of SRL models that use word, character and morphology level information and analyze how performance of characters compare to words and morphology for several languages. We conduct an in-depth error analysis for each morphological typology and analyze the strengths and limitations of character-level models that relate to out-of-domain data, training data size, long range dependencies and model complexity. Our exhaustive analyses shed light on important characteristics of character-level models and their semantic capability.
Anthology ID:
P18-1036
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
386–396
Language:
URL:
https://aclanthology.org/P18-1036
DOI:
10.18653/v1/P18-1036
Bibkey:
Cite (ACL):
Gözde Gül Şahin and Mark Steedman. 2018. Character-Level Models versus Morphology in Semantic Role Labeling. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 386–396, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Character-Level Models versus Morphology in Semantic Role Labeling (Şahin & Steedman, ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1036.pdf
Poster:
 P18-1036.Poster.pdf
Code
 gozdesahin/Subword_Semantic_Role_Labeling