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    <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.</abstract>
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%0 Conference Proceedings
%T Character-Level Models versus Morphology in Semantic Role Labeling
%A Şahin, Gözde Gül
%A Steedman, Mark
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F sahin-steedman-2018-character
%X 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.
%R 10.18653/v1/P18-1036
%U https://aclanthology.org/P18-1036
%U https://doi.org/10.18653/v1/P18-1036
%P 386-396

Markdown (Informal)

[Character-Level Models versus Morphology in Semantic Role Labeling](https://aclanthology.org/P18-1036) (Şahin & Steedman, ACL 2018)

• Character-Level Models versus Morphology in Semantic Role Labeling (Şahin & Steedman, ACL 2018)

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.