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natural-language-processing-2

Multilingual Word Embeddings

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Unsupervised Multilingual Word Embeddings

Claire Cardie, Xilun Chen•Tue Jul 31 2018

This work proposes a fully unsupervised framework for learning MWEs that directly exploits the relations between all language pairs and substantially outperforms previous approaches in the experiments on multilingual word translation and cross-lingual word similarity.

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SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings

Hinrich Schütze, Masoud Jalili Sabet, Philipp Dufter•Fri Apr 17 2020

This work proposes word alignment methods that require no parallel data and finds that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners – even with abundant parallel data.

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ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge

R. Speer, Joanna Lowry-Duda•Mon Apr 10 2017

Luminoso’s participation in SemEval 2017 Task 2, “Multilingual and Cross-lingual Semantic Word Similarity”, with a system based on ConceptNet, which took first place in both subtasks.

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Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach

Anoop Kunchukuttan, Pratik Jawanpuria, Arjun Balgovind, Bamdev Mishra•Sun Aug 26 2018

This work proposes a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary that outperforms previous approaches on the bilingual lexicon induction and cross-lingual word similarity tasks.

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Massively Multilingual Word Embeddings

Chris Dyer, Noah A. Smith, Guillaume Lample, Bridger Waleed Ammar, Yulia Tsvetkov, George Mulcaire•Thu Feb 04 2016

New methods for estimating and evaluating embeddings of words in more than fifty languages in a single shared embedding space are introduced and a new evaluation method is shown to correlate better than previous ones with two downstream tasks.

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ALL-IN-1: Short Text Classification with One Model for All Languages

Barbara Plank•Wed Oct 25 2017

This work presents ALL-IN-1, a simple model for multilingual text classification that does not require any parallel data, based on a traditional Support Vector Machine classifier exploiting multilingual word embeddings and character n-grams.

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Cross-lingual Lexical Sememe Prediction

Zhiyuan Liu, Yankai Lin, Maosong Sun, Fanchao Qi, Hao Zhu, Ruobing Xie•Sun Dec 31 2017

This work proposes a novel framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction, and shows that the proposed model achieves consistent and significant improvements as compared to baseline methods in cross-lingUAL sememe Prediction.

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Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task

K. Aberer, R. Lebret, Alireza Mohammadshahi•Mon Oct 07 2019

It is shown that the proposed new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages enables better generalization, achieving state-of-the-art performance in text-to-image and image- to-text retrieval task, and caption-caption similarity task.

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Multilingual Word Embeddings | State-of-the-Art