Word Translation
39 papers with code • 0 benchmarks • 3 datasets
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Word Translation Without Parallel Data
We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation.
Non-Adversarial Unsupervised Word Translation
We present a novel method that first aligns the second moment of the word distributions of the two languages and then iteratively refines the alignment.
Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion
Continuous word representations learned separately on distinct languages can be aligned so that their words become comparable in a common space.
Unsupervised Multilingual Word Embeddings
Multilingual Word Embeddings (MWEs) represent words from multiple languages in a single distributional vector space.
Cross-Lingual Adaptation using Structural Correspondence Learning
From these correspondences a cross-lingual representation is created that enables the transfer of classification knowledge from the source to the target language.
Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach
Our approach decouples learning the transformation from the source language to the target language into (a) learning rotations for language-specific embeddings to align them to a common space, and (b) learning a similarity metric in the common space to model similarities between the embeddings.
Robust Cross-lingual Embeddings from Parallel Sentences
Recent advances in cross-lingual word embeddings have primarily relied on mapping-based methods, which project pretrained word embeddings from different languages into a shared space through a linear transformation.
LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons
Data scarcity in low-resource languages can be addressed with word-to-word translations from labeled task data in high-resource languages using bilingual lexicons.
A Resource-Light Method for Cross-Lingual Semantic Textual Similarity
In contrast, we propose an unsupervised and a very resource-light approach for measuring semantic similarity between texts in different languages.
Unsupervised Clinical Language Translation
As patients' access to their doctors' clinical notes becomes common, translating professional, clinical jargon to layperson-understandable language is essential to improve patient-clinician communication.