Cross-Lingual Transfer
289 papers with code • 1 benchmarks • 16 datasets
Cross-lingual transfer refers to transfer learning using data and models available for one language for which ample such resources are available (e.g., English) to solve tasks in another, commonly more low-resource, language.
Benchmarks
These leaderboards are used to track progress in Cross-Lingual Transfer
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Libraries
Use these libraries to find Cross-Lingual Transfer models and implementations
CoNLL
PAWS-X
TyDiQA
MLQA
CoNLL 2002
Tatoeba
TyDiQA-GoldP
Latest papers
Measuring Cross-lingual Transfer in Bytes
We also found evidence that this transfer is not related to language contamination or language proximity, which strengthens the hypothesis that the model also relies on language-agnostic knowledge.
Adaptive Cross-lingual Text Classification through In-Context One-Shot Demonstrations
Zero-Shot Cross-lingual Transfer (ZS-XLT) utilizes a model trained in a source language to make predictions in another language, often with a performance loss.
AAdaM at SemEval-2024 Task 1: Augmentation and Adaptation for Multilingual Semantic Textual Relatedness
This paper presents our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages.
An Efficient Approach for Studying Cross-Lingual Transfer in Multilingual Language Models
The capacity and effectiveness of pre-trained multilingual models (MLMs) for zero-shot cross-lingual transfer is well established.
Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets
csu-signal/xlingual-robustness
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Multilingual Language Models (MLLMs) exhibit robust cross-lingual transfer capabilities, or the ability to leverage information acquired in a source language and apply it to a target language.
MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation
Our findings reveal that (i) current NNRs, even when based on a multilingual language model, suffer from substantial performance losses under ZS-XLT and that (ii) inclusion of target-language data in FS-XLT training has limited benefits, particularly when combined with a bilingual news consumption.
Tracing the Roots of Facts in Multilingual Language Models: Independent, Shared, and Transferred Knowledge
Acquiring factual knowledge for language models (LMs) in low-resource languages poses a serious challenge, thus resorting to cross-lingual transfer in multilingual LMs (ML-LMs).
From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models
To date, toxicity mitigation in language models has almost entirely been focused on single-language settings.
IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators
In particular, most mainstream Code-LMs have been pre-trained on source code files alone.
Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish
While misinformation is prevalent in other languages, the majority of research in this field has concentrated on the English language.
