natural-language-processing-11

Word Translation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in word-translation-11

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Libraries

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Use these libraries to find word-translation-11 models and implementations

facebookresearch/MUSE

2 papers 3,168

Datasets

Soundscape Attributes Translation Project (SATP) Dataset

satp-zsm-stage1

satp-zsm-stage2

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No subtasks available.

Most implemented papers

Word Translation Without Parallel Data

Marc'Aurelio Ranzato, Herv'e J'egou, Alexis Conneau, Guillaume Lample, Ludovic Denoyer•Tue Oct 10 2017

It is shown that a bilingual dictionary can be built between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way.

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Non-Adversarial Unsupervised Word Translation

Lior Wolf, Yedid Hoshen•Wed Jan 17 2018

A novel method is presented that first aligns the second moment of the word distributions of the two languages and then iteratively refines the alignment, which achieves better performance than recent state-of-the-art deep adversarial approaches and is competitive with the supervised baseline.

121 0

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Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion

Edouard Grave, Piotr Bojanowski, Armand Joulin, Tomas Mikolov, H. Jégou•Sat Mar 31 2018

This paper proposes an unified formulation that directly optimizes a retrieval criterion in an end-to-end fashion for word translation, and shows that this approach outperforms the state of the art on word translation.

321 0

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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.

138 0

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Cross-Lingual Adaptation Using Structural Correspondence Learning

Benno Stein, P. Prettenhofer•Tue Aug 03 2010

An extension of Structural Correspondence Learning (SCL), a recently proposed algorithm for domain adaptation, is described for cross-lingual adaptation in the context of text classification, showing a significant improvement of the proposed method over a machine translation baseline.

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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.

79 0

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Robust Cross-lingual Embeddings from Parallel Sentences

Martin Jaggi, Robert West, A. Sabet, Prakhar Gupta, Jean-Baptiste Cordonnier•Tue Sep 24 2019

This work proposes a bilingual extension of the CBOW method which leverages sentence-aligned corpora to obtain robust cross-lingual word and sentence representations and significantly improves cross-lingsual sentence retrieval performance over all other approaches while maintaining parity with the current state-of-the-art methods on word-translation.

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LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons

Zheng-Xin Yong, Cristina Menghini, Stephen H. Bach•Tue Feb 20 2024

LexC-Gen serves as a potential solution to close the performance gap between open-source multilingual models, such as BLOOMZ and Aya-101, and state-of-the-art commercial models like GPT-4o on low-resource-language tasks.

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A resource-light method for cross-lingual semantic textual similarity

Paolo Rosso, Goran Glavas, Marc Franco-Salvador, Simone Paolo Ponzetto•Thu Nov 30 2017

An unsupervised and a very resource-light approach for measuring semantic similarity between texts in different languages, applicable to virtually any pair of languages for which there exists a sufficiently large corpus, required to learn monolingual word embeddings.

44 0

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Unsupervised Clinical Language Translation

Peter Szolovits, W. Weng, Yu-An Chung•Sun Feb 03 2019

This work shows that a framework using representation learning, bilingual dictionary induction and statistical machine translation yields the best precision at 10 of 0.827 on professional-to-consumer word translation, and mean opinion scores of 4.10 and 4.28 out of 5 for clinical correctness and layperson readability, respectively, on sentence translation.

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