SOTAVerified

Cross-Lingual Transfer

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.

Papers

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Showing 101–125 of 782 papers

TitleStatusHype
On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation EvaluationCode1
Gender Bias in Multilingual Embeddings and Cross-Lingual TransferCode1
XCOPA: A Multilingual Dataset for Causal Commonsense ReasoningCode1
An Empirical Study of Pre-trained Transformers for Arabic Information ExtractionCode1
End-to-End Slot Alignment and Recognition for Cross-Lingual NLUCode1
A Chinese Corpus for Fine-grained Entity TypingCode1
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual GeneralizationCode1
Zero-Shot Cross-Lingual Transfer with Meta LearningCode1
Parameter Space Factorization for Zero-Shot Learning across Tasks and LanguagesCode1
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual GeneralisationCode1
Unsupervised Cross-lingual Representation Learning at ScaleCode1
Cross-Lingual Natural Language Generation via Pre-TrainingCode1
Choosing Transfer Languages for Cross-Lingual LearningCode1
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERTCode1
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and BeyondCode1
Enhancing Cross-task Transfer of Large Language Models via Activation Steering—0
HanjaBridge: Resolving Semantic Ambiguity in Korean LLMs via Hanja-Augmented Pre-Training—0
Cross-Lingual Transfer of Cultural Knowledge: An Asymmetric Phenomenon—0
Speech-to-Text Translation with Phoneme-Augmented CoT: Enhancing Cross-Lingual Transfer in Low-Resource Scenarios—0
Multilinguality Does not Make Sense: Investigating Factors Behind Zero-Shot Transfer in Sense-Aware Tasks—0
LLMs Are Globally Multilingual Yet Locally Monolingual: Exploring Knowledge Transfer via Language and Thought Theory—0
Limited-Resource Adapters Are Regularizers, Not Linguists—0
SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods—0
The Unreasonable Effectiveness of Model Merging for Cross-Lingual Transfer in LLMs—0
Semantic Pivots Enable Cross-Lingual Transfer in Large Language Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PaLM 2 (few-shot)Accuracy94.4—Unverified
2mT0-13BAccuracy84.45—Unverified
3RoBERTa Large (translate test)Accuracy76.05—Unverified
4BLOOMZAccuracy75.5—Unverified
5MAD-X BaseAccuracy60.94—Unverified
6mGPTAccuracy55.5—Unverified