SOTAVerified

Cross-Lingual Document Classification

Cross-lingual document classification refers to the task of using data and models available for one language for which ample such resources are available (e.g., English) to solve classification tasks in another, commonly low-resource, language.

Papers

Showing 110 of 25 papers

TitleStatusHype
Multilingual and cross-lingual document classification: A meta-learning approachCode1
Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling0
Robust Cross-lingual Embeddings from Parallel SentencesCode0
Wasserstein distances for evaluating cross-lingual embeddings0
ZeRO: Memory Optimizations Toward Training Trillion Parameter ModelsCode1
Bridging the domain gap in cross-lingual document classificationCode0
MultiFiT: Efficient Multi-lingual Language Model Fine-tuningCode1
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and BeyondCode1
Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification0
Variational learning across domains with triplet information0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1XLMft UDAAccuracy96.8Unverified
2MultiFiT, pseudoAccuracy79.1Unverified
3Massively Multilingual Sentence EmbeddingsAccuracy77.33Unverified
4MultiCCA + CNNAccuracy72.5Unverified
5BiLSTM (UN)Accuracy69.5Unverified
6BiLSTM (Europarl)Accuracy66.65Unverified