@inproceedings{kementchedjhieva-lopez-2018-indicatements,
title = "{`}Indicatements{'} that character language models learn {E}nglish morpho-syntactic units and regularities",
author = "Kementchedjhieva, Yova and
Lopez, Adam",
editor = "Linzen, Tal and
Chrupa{\l}a, Grzegorz and
Alishahi, Afra",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5417",
doi = "10.18653/v1/W18-5417",
pages = "145--153",
abstract = "Character language models have access to surface morphological patterns, but it is not clear whether or \textit{how} they learn abstract morphological regularities. We instrument a character language model with several probes, finding that it can develop a specific unit to identify word boundaries and, by extension, morpheme boundaries, which allows it to capture linguistic properties and regularities of these units. Our language model proves surprisingly good at identifying the selectional restrictions of English derivational morphemes, a task that requires both morphological and syntactic awareness. Thus we conclude that, when morphemes overlap extensively with the words of a language, a character language model can perform morphological abstraction.",
}
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<abstract>Character language models have access to surface morphological patterns, but it is not clear whether or how they learn abstract morphological regularities. We instrument a character language model with several probes, finding that it can develop a specific unit to identify word boundaries and, by extension, morpheme boundaries, which allows it to capture linguistic properties and regularities of these units. Our language model proves surprisingly good at identifying the selectional restrictions of English derivational morphemes, a task that requires both morphological and syntactic awareness. Thus we conclude that, when morphemes overlap extensively with the words of a language, a character language model can perform morphological abstraction.</abstract>
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%0 Conference Proceedings
%T ‘Indicatements’ that character language models learn English morpho-syntactic units and regularities
%A Kementchedjhieva, Yova
%A Lopez, Adam
%Y Linzen, Tal
%Y Chrupała, Grzegorz
%Y Alishahi, Afra
%S Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kementchedjhieva-lopez-2018-indicatements
%X Character language models have access to surface morphological patterns, but it is not clear whether or how they learn abstract morphological regularities. We instrument a character language model with several probes, finding that it can develop a specific unit to identify word boundaries and, by extension, morpheme boundaries, which allows it to capture linguistic properties and regularities of these units. Our language model proves surprisingly good at identifying the selectional restrictions of English derivational morphemes, a task that requires both morphological and syntactic awareness. Thus we conclude that, when morphemes overlap extensively with the words of a language, a character language model can perform morphological abstraction.
%R 10.18653/v1/W18-5417
%U https://aclanthology.org/W18-5417
%U https://doi.org/10.18653/v1/W18-5417
%P 145-153
Markdown (Informal)
[‘Indicatements’ that character language models learn English morpho-syntactic units and regularities](https://aclanthology.org/W18-5417) (Kementchedjhieva & Lopez, EMNLP 2018)
ACL