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 626–650 of 782 papers

TitleStatusHype
The CMU-LTI submission to the SIGMORPHON 2020 Shared Task 0: Language-Specific Cross-Lingual Transfer—0
Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization—0
Self-Supervised Representations Improve End-to-End Speech Translation—0
Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated TasksCode0
Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation—0
Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment—0
English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too—0
English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too—0
Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learning—0
Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text—0
Cross-lingual Approaches for Task-specific Dialogue Act Recognition—0
Are All Languages Created Equal in Multilingual BERT?—0
Cross-lingual Transfer of Sentiment Classifiers—0
Fine-Grained Analysis of Cross-Linguistic Syntactic DivergencesCode0
DaNE: A Named Entity Resource for Danish—0
WikiBank: Using Wikidata to Improve Multilingual Frame-Semantic Parsing—0
Cross-lingual Structure Transfer for Zero-resource Event Extraction—0
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers—0
Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for Basque—0
Mutlitask Learning for Cross-Lingual Transfer of Semantic Dependencies—0
Don't Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings—0
Learning to Learn Morphological Inflection for Resource-Poor Languages—0
Weakly Supervised POS Taggers Perform Poorly on Truly Low-Resource Languages—0
Extending Multilingual BERT to Low-Resource Languages—0
UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP—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