Cross-lingual Dependency Parsing Based on Distributed Representations

Guo, Jifeng; Che, Wanxiang; Yarowsky, David; Wang, Haifeng; Liu, Ting

doi:10.3115/v1/p15-1119

Cited by 163 publications

(163 citation statements)

References 23 publications

(27 reference statements)

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“…Previous work has shown its effectiveness across a wide range of multilingual transfer tasks including tagging (Kim et al, 2015), syntactic parsing (Xiao and Guo, 2014;Guo et al, 2015;Durrett et al, 2012), and machine translation (Zou et al, 2013;Mikolov et al, 2013b). However, these approaches commonly require parallel sentences or bilingual lexicon to learn multilingual embeddings.…”

Section: Multilingual Word Embeddingsmentioning

confidence: 99%

Ten Pairs to Tag – Multilingual POS Tagging via Coarse Mapping between Embeddings

Zhang

Gaddy

Barzilay

et al. 2016

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

101

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In the absence of annotations in the target language, multilingual models typically draw on extensive parallel resources. In this paper, we demonstrate that accurate multilingual partof-speech (POS) tagging can be done with just a few (e.g., ten) word translation pairs. We use the translation pairs to establish a coarse linear isometric (orthonormal) mapping between monolingual embeddings. This enables the supervised source model expressed in terms of embeddings to be used directly on the target language. We further refine the model in an unsupervised manner by initializing and regularizing it to be close to the direct transfer model. Averaged across six languages, our model yields a 37.5% absolute improvement over the monolingual prototypedriven method (Haghighi and Klein, 2006) when using a comparable amount of supervision. Moreover, to highlight key linguistic characteristics of the generated tags, we use them to predict typological properties of languages, obtaining a 50% error reduction relative to the prototype model.

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Section: Multilingual Word Embeddingsmentioning

confidence: 99%

Ten Pairs to Tag – Multilingual POS Tagging via Coarse Mapping between Embeddings

Zhang

Gaddy

Barzilay

et al. 2016

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

101

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“…2). SBWES may be used to support many tasks, e.g., computing cross-lingual/multilingual semantic word similarity (Faruqui and Dyer, 2014), learning bilingual word lexicons (Mikolov et al, 2013a;Gouws et al, 2015;, cross-lingual entity linking (Tsai and Roth, 2016), parsing (Guo et al, 2015;Johannsen et al, 2015), machine translation (Zou et al, 2013), or crosslingual information retrieval (Vulić and Moens, 2015;Mitra et al, 2016).…”

Section: Monolingual Vs Bilingualmentioning

confidence: 99%

On the Role of Seed Lexicons in Learning Bilingual Word Embeddings

Vulić¹,

Korhonen²

2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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A shared bilingual word embedding space (SBWES) is an indispensable resource in a variety of cross-language NLP and IR tasks. A common approach to the SB-WES induction is to learn a mapping function between monolingual semantic spaces, where the mapping critically relies on a seed word lexicon used in the learning process. In this work, we analyze the importance and properties of seed lexicons for the SBWES induction across different dimensions (i.e., lexicon source, lexicon size, translation method, translation pair reliability). On the basis of our analysis, we propose a simple but effective hybrid bilingual word embedding (BWE) model. This model (HYBWE) learns the mapping between two monolingual embedding spaces using only highly reliable symmetric translation pairs from a seed document-level embedding space. We perform bilingual lexicon learning (BLL) with 3 language pairs and show that by carefully selecting reliable translation pairs our new HYBWE model outperforms benchmarking BWE learning models, all of which use more expensive bilingual signals. Effectively, we demonstrate that a SBWES may be induced by leveraging only a very weak bilingual signal (document alignments) along with monolingual data.

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“…We thus include word features using bilingual dictionaries -i.e. translating the words used as features into a single language (English) -, or through crosslingual word embeddings as proposed in (Guo et al, 2015) for dependency parsing. More precisely, we used the cross-lingual word representations presented in (Levy et al, 2017) that allow multi-source learning and have proven useful for POS tagging but also more semantic-oriented tasks, such as dependency parsing and document classification.…”

Section: Cross-lingual Discourse Parsingmentioning

confidence: 99%

Cross-lingual RST Discourse Parsing

Braud¹,

Coavoux²,

Søgaard³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating models from the English RST Discourse Treebank. However, discourse treebanks for other languages exist, including Spanish, German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same underlying linguistic theory, but differ slightly in the way documents are annotated. In this paper, we present (a) a new discourse parser which is simpler, yet competitive (significantly better on 2/3 metrics) to state of the art for English, (b) a harmonization of discourse treebanks across languages, enabling us to present (c) what to the best of our knowledge are the first experiments on crosslingual discourse parsing.

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Cross-lingual Dependency Parsing Based on Distributed Representations

Cited by 163 publications

References 23 publications

Ten Pairs to Tag – Multilingual POS Tagging via Coarse Mapping between Embeddings

Ten Pairs to Tag – Multilingual POS Tagging via Coarse Mapping between Embeddings

On the Role of Seed Lexicons in Learning Bilingual Word Embeddings

Cross-lingual RST Discourse Parsing

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