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        <title>Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources</title>
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        <namePart type="given">Joo-Kyung</namePart>
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        <namePart type="family">Sarikaya</namePart>
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Training a POS tagging model with crosslingual transfer learning usually requires linguistic knowledge and resources about the relation between the source language and the target language. In this paper, we introduce a cross-lingual transfer learning model for POS tagging without ancillary resources such as parallel corpora. The proposed cross-lingual model utilizes a common BLSTM that enables knowledge transfer from other languages, and private BLSTMs for language-specific representations. The cross-lingual model is trained with language-adversarial training and bidirectional language modeling as auxiliary objectives to better represent language-general information while not losing the information about a specific target language. Evaluating on POS datasets from 14 languages in the Universal Dependencies corpus, we show that the proposed transfer learning model improves the POS tagging performance of the target languages without exploiting any linguistic knowledge between the source language and the target language.</abstract>
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%0 Conference Proceedings
%T Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources
%A Kim, Joo-Kyung
%A Kim, Young-Bum
%A Sarikaya, Ruhi
%A Fosler-Lussier, Eric
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F kim-etal-2017-cross
%X Training a POS tagging model with crosslingual transfer learning usually requires linguistic knowledge and resources about the relation between the source language and the target language. In this paper, we introduce a cross-lingual transfer learning model for POS tagging without ancillary resources such as parallel corpora. The proposed cross-lingual model utilizes a common BLSTM that enables knowledge transfer from other languages, and private BLSTMs for language-specific representations. The cross-lingual model is trained with language-adversarial training and bidirectional language modeling as auxiliary objectives to better represent language-general information while not losing the information about a specific target language. Evaluating on POS datasets from 14 languages in the Universal Dependencies corpus, we show that the proposed transfer learning model improves the POS tagging performance of the target languages without exploiting any linguistic knowledge between the source language and the target language.
%R 10.18653/v1/D17-1302
%U https://aclanthology.org/D17-1302
%U https://doi.org/10.18653/v1/D17-1302
%P 2832-2838

Markdown (Informal)

[Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources](https://aclanthology.org/D17-1302) (Kim et al., EMNLP 2017)

• Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources (Kim et al., EMNLP 2017)

ACL

• Joo-Kyung Kim, Young-Bum Kim, Ruhi Sarikaya, and Eric Fosler-Lussier. 2017. Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2832–2838, Copenhagen, Denmark. Association for Computational Linguistics.