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        <title>Cross-Lingual Word Representations: Induction and Evaluation</title>
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            <namePart type="given">Nathan</namePart>
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    <abstract>In recent past, NLP as a field has seen tremendous utility of distributional word vector representations as features in downstream tasks. The fact that these word vectors can be trained on unlabeled monolingual corpora of a language makes them an inexpensive resource in NLP. With the increasing use of monolingual word vectors, there is a need for word vectors that can be used as efficiently across multiple languages as monolingually. Therefore, learning bilingual and multilingual word embeddings/vectors is currently an important research topic. These vectors offer an elegant and language-pair independent way to represent content across different languages.This tutorial aims to bring NLP researchers up to speed with the current techniques in cross-lingual word representation learning. We will first discuss how to induce cross-lingual word representations (covering both bilingual and multilingual ones) from various data types and resources (e.g., parallel data, comparable data, non-aligned monolingual data in different languages, dictionaries and theasuri, or, even, images, eye-tracking data). We will then discuss how to evaluate such representations, intrinsically and extrinsically. We will introduce researchers to state-of-the-art methods for constructing cross-lingual word representations and discuss their applicability in a broad range of downstream NLP applications.We will deliver a detailed survey of the current methods, discuss best training and evaluation practices and use-cases, and provide links to publicly available implementations, datasets, and pre-trained models.</abstract>
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%0 Conference Proceedings
%T Cross-Lingual Word Representations: Induction and Evaluation
%A Faruqui, Manaal
%A Søgaard, Anders
%A Vulić, Ivan
%Y Birch, Alexandra
%Y Schneider, Nathan
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F faruqui-etal-2017-cross
%X In recent past, NLP as a field has seen tremendous utility of distributional word vector representations as features in downstream tasks. The fact that these word vectors can be trained on unlabeled monolingual corpora of a language makes them an inexpensive resource in NLP. With the increasing use of monolingual word vectors, there is a need for word vectors that can be used as efficiently across multiple languages as monolingually. Therefore, learning bilingual and multilingual word embeddings/vectors is currently an important research topic. These vectors offer an elegant and language-pair independent way to represent content across different languages.This tutorial aims to bring NLP researchers up to speed with the current techniques in cross-lingual word representation learning. We will first discuss how to induce cross-lingual word representations (covering both bilingual and multilingual ones) from various data types and resources (e.g., parallel data, comparable data, non-aligned monolingual data in different languages, dictionaries and theasuri, or, even, images, eye-tracking data). We will then discuss how to evaluate such representations, intrinsically and extrinsically. We will introduce researchers to state-of-the-art methods for constructing cross-lingual word representations and discuss their applicability in a broad range of downstream NLP applications.We will deliver a detailed survey of the current methods, discuss best training and evaluation practices and use-cases, and provide links to publicly available implementations, datasets, and pre-trained models.
%U https://aclanthology.org/D17-3007

Markdown (Informal)

[Cross-Lingual Word Representations: Induction and Evaluation](https://aclanthology.org/D17-3007) (Faruqui et al., EMNLP 2017)

• Cross-Lingual Word Representations: Induction and Evaluation (Faruqui et al., EMNLP 2017)

ACL

• Manaal Faruqui, Anders Søgaard, and Ivan Vulić. 2017. Cross-Lingual Word Representations: Induction and Evaluation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, Copenhagen, Denmark. Association for Computational Linguistics.