@inproceedings{klein-tsarfaty-2020-getting,
title = "Getting the {\#}{\#}life out of living: How Adequate Are Word-Pieces for Modelling Complex Morphology?",
author = "Klein, Stav and
Tsarfaty, Reut",
editor = "Nicolai, Garrett and
Gorman, Kyle and
Cotterell, Ryan",
booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sigmorphon-1.24",
doi = "10.18653/v1/2020.sigmorphon-1.24",
pages = "204--209",
abstract = "This work investigates the most basic units that underlie contextualized word embeddings, such as BERT {---} the so-called word pieces. In Morphologically-Rich Languages (MRLs) which exhibit morphological fusion and non-concatenative morphology, the different units of meaning within a word may be fused, intertwined, and cannot be separated linearly. Therefore, when using word-pieces in MRLs, we must consider that: (1) a linear segmentation into sub-word units might not capture the full morphological complexity of words; and (2) representations that leave morphological knowledge on sub-word units inaccessible might negatively affect performance. Here we empirically examine the capacity of word-pieces to capture morphology by investigating the task of multi-tagging in Modern Hebrew, as a proxy to evaluate the underlying segmentation. Our results show that, while models trained to predict multi-tags for complete words outperform models tuned to predict the distinct tags of WPs, we can improve the WPs tag prediction by purposefully constraining the word-pieces to reflect their internal functions. We suggest that linguistically-informed word-pieces schemes, that make the morphological structure explicit, might boost performance for MRLs.",
}
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%0 Conference Proceedings
%T Getting the ##life out of living: How Adequate Are Word-Pieces for Modelling Complex Morphology?
%A Klein, Stav
%A Tsarfaty, Reut
%Y Nicolai, Garrett
%Y Gorman, Kyle
%Y Cotterell, Ryan
%S Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F klein-tsarfaty-2020-getting
%X This work investigates the most basic units that underlie contextualized word embeddings, such as BERT — the so-called word pieces. In Morphologically-Rich Languages (MRLs) which exhibit morphological fusion and non-concatenative morphology, the different units of meaning within a word may be fused, intertwined, and cannot be separated linearly. Therefore, when using word-pieces in MRLs, we must consider that: (1) a linear segmentation into sub-word units might not capture the full morphological complexity of words; and (2) representations that leave morphological knowledge on sub-word units inaccessible might negatively affect performance. Here we empirically examine the capacity of word-pieces to capture morphology by investigating the task of multi-tagging in Modern Hebrew, as a proxy to evaluate the underlying segmentation. Our results show that, while models trained to predict multi-tags for complete words outperform models tuned to predict the distinct tags of WPs, we can improve the WPs tag prediction by purposefully constraining the word-pieces to reflect their internal functions. We suggest that linguistically-informed word-pieces schemes, that make the morphological structure explicit, might boost performance for MRLs.
%R 10.18653/v1/2020.sigmorphon-1.24
%U https://aclanthology.org/2020.sigmorphon-1.24
%U https://doi.org/10.18653/v1/2020.sigmorphon-1.24
%P 204-209
Markdown (Informal)
[Getting the ##life out of living: How Adequate Are Word-Pieces for Modelling Complex Morphology?](https://aclanthology.org/2020.sigmorphon-1.24) (Klein & Tsarfaty, SIGMORPHON 2020)
ACL