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        <title>Cross-Lingual Dependency Parsing with Unlabeled Auxiliary Languages</title>
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        <namePart type="given">Wasi</namePart>
        <namePart type="given">Uddin</namePart>
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    <name type="personal">
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            <namePart type="given">Mohit</namePart>
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    <abstract>Cross-lingual transfer learning has become an important weapon to battle the unavailability of annotated resources for low-resource languages. One of the fundamental techniques to transfer across languages is learning language-agnostic representations, in the form of word embeddings or contextual encodings. In this work, we propose to leverage unannotated sentences from auxiliary languages to help learning language-agnostic representations. Specifically, we explore adversarial training for learning contextual encoders that produce invariant representations across languages to facilitate cross-lingual transfer. We conduct experiments on cross-lingual dependency parsing where we train a dependency parser on a source language and transfer it to a wide range of target languages. Experiments on 28 target languages demonstrate that adversarial training significantly improves the overall transfer performances under several different settings. We conduct a careful analysis to evaluate the language-agnostic representations resulted from adversarial training.</abstract>
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%0 Conference Proceedings
%T Cross-Lingual Dependency Parsing with Unlabeled Auxiliary Languages
%A Ahmad, Wasi Uddin
%A Zhang, Zhisong
%A Ma, Xuezhe
%A Chang, Kai-Wei
%A Peng, Nanyun
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F ahmad-etal-2019-cross
%X Cross-lingual transfer learning has become an important weapon to battle the unavailability of annotated resources for low-resource languages. One of the fundamental techniques to transfer across languages is learning language-agnostic representations, in the form of word embeddings or contextual encodings. In this work, we propose to leverage unannotated sentences from auxiliary languages to help learning language-agnostic representations. Specifically, we explore adversarial training for learning contextual encoders that produce invariant representations across languages to facilitate cross-lingual transfer. We conduct experiments on cross-lingual dependency parsing where we train a dependency parser on a source language and transfer it to a wide range of target languages. Experiments on 28 target languages demonstrate that adversarial training significantly improves the overall transfer performances under several different settings. We conduct a careful analysis to evaluate the language-agnostic representations resulted from adversarial training.
%R 10.18653/v1/K19-1035
%U https://aclanthology.org/K19-1035
%U https://doi.org/10.18653/v1/K19-1035
%P 372-382

Markdown (Informal)

[Cross-Lingual Dependency Parsing with Unlabeled Auxiliary Languages](https://aclanthology.org/K19-1035) (Ahmad et al., CoNLL 2019)

• Cross-Lingual Dependency Parsing with Unlabeled Auxiliary Languages (Ahmad et al., CoNLL 2019)

ACL

• Wasi Uddin Ahmad, Zhisong Zhang, Xuezhe Ma, Kai-Wei Chang, and Nanyun Peng. 2019. Cross-Lingual Dependency Parsing with Unlabeled Auxiliary Languages. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 372–382, Hong Kong, China. Association for Computational Linguistics.