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        <title>Multilingual Constituency Parsing with Self-Attention and Pre-Training</title>
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    <abstract>We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions. We first compare the benefits of no pre-training, fastText, ELMo, and BERT for English and find that BERT outperforms ELMo, in large part due to increased model capacity, whereas ELMo in turn outperforms the non-contextual fastText embeddings. We also find that pre-training is beneficial across all 11 languages tested; however, large model sizes (more than 100 million parameters) make it computationally expensive to train separate models for each language. To address this shortcoming, we show that joint multilingual pre-training and fine-tuning allows sharing all but a small number of parameters between ten languages in the final model. The 10x reduction in model size compared to fine-tuning one model per language causes only a 3.2% relative error increase in aggregate. We further explore the idea of joint fine-tuning and show that it gives low-resource languages a way to benefit from the larger datasets of other languages. Finally, we demonstrate new state-of-the-art results for 11 languages, including English (95.8 F1) and Chinese (91.8 F1).</abstract>
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%0 Conference Proceedings
%T Multilingual Constituency Parsing with Self-Attention and Pre-Training
%A Kitaev, Nikita
%A Cao, Steven
%A Klein, Dan
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 jul
%I Association for Computational Linguistics
%C Florence, Italy
%F kitaev-etal-2019-multilingual
%X We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions. We first compare the benefits of no pre-training, fastText, ELMo, and BERT for English and find that BERT outperforms ELMo, in large part due to increased model capacity, whereas ELMo in turn outperforms the non-contextual fastText embeddings. We also find that pre-training is beneficial across all 11 languages tested; however, large model sizes (more than 100 million parameters) make it computationally expensive to train separate models for each language. To address this shortcoming, we show that joint multilingual pre-training and fine-tuning allows sharing all but a small number of parameters between ten languages in the final model. The 10x reduction in model size compared to fine-tuning one model per language causes only a 3.2% relative error increase in aggregate. We further explore the idea of joint fine-tuning and show that it gives low-resource languages a way to benefit from the larger datasets of other languages. Finally, we demonstrate new state-of-the-art results for 11 languages, including English (95.8 F1) and Chinese (91.8 F1).
%R 10.18653/v1/P19-1340
%U https://aclanthology.org/P19-1340
%U https://doi.org/10.18653/v1/P19-1340
%P 3499-3505

Markdown (Informal)

[Multilingual Constituency Parsing with Self-Attention and Pre-Training](https://aclanthology.org/P19-1340) (Kitaev et al., ACL 2019)

• Multilingual Constituency Parsing with Self-Attention and Pre-Training (Kitaev et al., ACL 2019)

ACL

• Nikita Kitaev, Steven Cao, and Dan Klein. 2019. Multilingual Constituency Parsing with Self-Attention and Pre-Training. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3499–3505, Florence, Italy. Association for Computational Linguistics.