Cross-Lingual Document Classification
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Benchmarks
Most implemented
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning
Robust Cross-lingual Embeddings from Parallel Sentences
A Corpus for Multilingual Document Classification in Eight Languages
Papers
Multilingual and cross-lingual document classification: A meta-learning approach
The great majority of languages in the world are considered under-resourced for the successful application of deep learning methods. In this work, we propose a meta-learning approach to document classification in limited…
Cross-Lingual Document ClassificationDocument ClassificationGeneral ClassificationMeta-LearningMargin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling
Unsupervised domain adaptation addresses the problem of leveraging labeled data in a source domain to learn a well-performing model in a target domain where labels are unavailable. In this paper, we improve upon a recent…
Cross-Lingual Document ClassificationDocument ClassificationDomain AdaptationNER+1Robust 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. Howe…
Cross-Lingual Document ClassificationCross-Lingual Word EmbeddingsDocument ClassificationRetrieval+6Wasserstein distances for evaluating cross-lingual embeddings
Word embeddings are high dimensional vector representations of words that capture their semantic similarity in the vector space. There exist several algorithms for learning such embeddings both for a single language as w…
Cross-Lingual Document ClassificationDocument ClassificationRetrievalSemantic Similarity+2ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
Large deep learning models offer significant accuracy gains, but training billions to trillions of parameters is challenging. Existing solutions such as data and model parallelisms exhibit fundamental limitations to fit …
Cross-Lingual Document ClassificationImage GenerationLanguage ModellingBridging the domain gap in cross-lingual document classification
The scarcity of labeled training data often prohibits the internationalization of NLP models to multiple languages. Recent developments in cross-lingual understanding (XLU) has made progress in this area, trying to bridg…
ClassificationCross-Domain Document ClassificationCross-Lingual Document ClassificationCross-Lingual Sentiment Classification+5