@inproceedings{jaidi-etal-2022-impact,
title = "Impact of Sequence Length and Copying on Clause-Level Inflection",
author = "Jaidi, Badr and
Saboo, Utkarsh and
Wu, Xihan and
Nicolai, Garrett and
Silfverberg, Miikka",
editor = {Ataman, Duygu and
Gonen, Hila and
Ruder, Sebastian and
Firat, Orhan and
G{\"u}l Sahin, G{\"o}zde and
Mirzakhalov, Jamshidbek},
booktitle = "Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.mrl-1.11",
doi = "10.18653/v1/2022.mrl-1.11",
pages = "106--114",
abstract = "We present the University of British Columbia{'}s submission to the MRL shared task on multilingual clause-level morphology. Our submission extends word-level inflectional models to the clause-level in two ways: first, by evaluating the role that BPE has on the learning of inflectional morphology, and second, by evaluating the importance of a copy bias obtained through data hallucination. Experiments demonstrate a strong preference for language-tuned BPE and a copy bias over a vanilla transformer. The methods are complementary for inflection and analysis tasks {--} combined models see error reductions of 38{\%} for inflection and 15.6{\%} for analysis; However, this synergy does not hold for reinflection, which performs best under a BPE-only setting. A deeper analysis of the errors generated by our models illustrates that the copy bias may be too strong - the combined model produces predictions more similar to the copy-influenced system, despite the success of the BPE-model.",
}
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<abstract>We present the University of British Columbia’s submission to the MRL shared task on multilingual clause-level morphology. Our submission extends word-level inflectional models to the clause-level in two ways: first, by evaluating the role that BPE has on the learning of inflectional morphology, and second, by evaluating the importance of a copy bias obtained through data hallucination. Experiments demonstrate a strong preference for language-tuned BPE and a copy bias over a vanilla transformer. The methods are complementary for inflection and analysis tasks – combined models see error reductions of 38% for inflection and 15.6% for analysis; However, this synergy does not hold for reinflection, which performs best under a BPE-only setting. A deeper analysis of the errors generated by our models illustrates that the copy bias may be too strong - the combined model produces predictions more similar to the copy-influenced system, despite the success of the BPE-model.</abstract>
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%0 Conference Proceedings
%T Impact of Sequence Length and Copying on Clause-Level Inflection
%A Jaidi, Badr
%A Saboo, Utkarsh
%A Wu, Xihan
%A Nicolai, Garrett
%A Silfverberg, Miikka
%Y Ataman, Duygu
%Y Gonen, Hila
%Y Ruder, Sebastian
%Y Firat, Orhan
%Y Gül Sahin, Gözde
%Y Mirzakhalov, Jamshidbek
%S Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F jaidi-etal-2022-impact
%X We present the University of British Columbia’s submission to the MRL shared task on multilingual clause-level morphology. Our submission extends word-level inflectional models to the clause-level in two ways: first, by evaluating the role that BPE has on the learning of inflectional morphology, and second, by evaluating the importance of a copy bias obtained through data hallucination. Experiments demonstrate a strong preference for language-tuned BPE and a copy bias over a vanilla transformer. The methods are complementary for inflection and analysis tasks – combined models see error reductions of 38% for inflection and 15.6% for analysis; However, this synergy does not hold for reinflection, which performs best under a BPE-only setting. A deeper analysis of the errors generated by our models illustrates that the copy bias may be too strong - the combined model produces predictions more similar to the copy-influenced system, despite the success of the BPE-model.
%R 10.18653/v1/2022.mrl-1.11
%U https://aclanthology.org/2022.mrl-1.11
%U https://doi.org/10.18653/v1/2022.mrl-1.11
%P 106-114
Markdown (Informal)
[Impact of Sequence Length and Copying on Clause-Level Inflection](https://aclanthology.org/2022.mrl-1.11) (Jaidi et al., MRL 2022)
ACL
- Badr Jaidi, Utkarsh Saboo, Xihan Wu, Garrett Nicolai, and Miikka Silfverberg. 2022. Impact of Sequence Length and Copying on Clause-Level Inflection. In Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL), pages 106–114, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.