Cross-Lingual Document Classification
12 papers with code • 10 benchmarks • 2 datasets
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.
We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts.
Large deep learning models offer significant accuracy gains, but training billions to trillions of parameters is challenging.
We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple and computationally-efficient model for learning bilingual distributed representations of words which can scale to large monolingual datasets and does not require word-aligned parallel training data.
To tackle the sentiment classification problem in low-resource languages without adequate annotated data, we propose an Adversarial Deep Averaging Network (ADAN) to transfer the knowledge learned from labeled data on a resource-rich source language to low-resource languages where only unlabeled data exists.
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.
Distributed representations of meaning are a natural way to encode covariance relationships between words and phrases in NLP.
We present a novel technique for learning semantic representations, which extends the distributional hypothesis to multilingual data and joint-space embeddings.
Crosslingual word embeddings represent lexical items from different languages in the same vector space, enabling transfer of NLP tools.