Papers Cross-Lingual Document Classification

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…

Margin-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…

Robust 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…

Wasserstein 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…

ZeRO: 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 …

Bridging 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…

MultiFiT: Efficient Multi-lingual Language Model Fine-tuning

Pretrained language models are promising particularly for low-resource languages as they only require unlabelled data. However, training existing models requires huge amounts of compute, while pretrained cross-lingual mo…

Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond

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. Our system uses a single BiLSTM encode…

Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification

Text classification must sometimes be applied in a low-resource language with no labeled training data. However, training data may be available in a related language. We investigate whether character-level knowledge tran…

Variational learning across domains with triplet information

The work investigates deep generative models, which allow us to use training data from one domain to build a model for another domain. We propose the Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint d…

NMT-based Cross-lingual Document Embeddings

This paper investigates a cross-lingual document embedding method that improves the current Neural machine Translation framework based Document Vector (NTDV or simply NV). NV is developed with a self-attention mechanism …

A Multi-task Approach to Learning Multilingual Representations

We present a novel multi-task modeling approach to learning multilingual distributed representations of text. Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together wit…

Multilingual Seq2seq Training with Similarity Loss for Cross-Lingual Document Classification

In this paper we continue experiments where neural machine translation training is used to produce joint cross-lingual fixed-dimensional sentence embeddings. In this framework we introduce a simple method of adding a los…

Variational learning across domains with triplet information

The work investigates deep generative models, which allow us to use training data from one domain to build a model for another domain. We propose the Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint d…

A Corpus for Multilingual Document Classification in Eight Languages

Cross-lingual document classification aims at training a document classifier on resources in one language and transferring it to a different language without any additional resources. Several approaches have been propose…

KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus

Learning Monolingual Compositional Representations via Bilingual Supervision

Learning Crosslingual Word Embeddings without Bilingual Corpora

Crosslingual word embeddings represent lexical items from different languages in the same vector space, enabling transfer of NLP tools. However, previous attempts had expensive resource requirements, difficulty incorpora…

Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

In recent years great success has been achieved in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most languages do not enjoy such an abundance of …

Learning Cross-lingual Representations with Matrix Factorization