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task ID: crosslingualadaptation

Related Papers

Semi-Supervised Representation Learning for Cross-Lingual Text Classification

Authors: nan

Publication: TIPSTER (2013)

DOI: nan

Cross-lingual adaptation aims to learn a pre- diction model in a label-scarce target lan- guage by exploiting labeled data from a label- rich source language. An effective cross- lingual adaptation …...

34621

Unsupervised Cross-Lingual Adaptation of Dependency Parsers Using CRF Autoencoders

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractWe consider the task of cross-lingual adaptation of dependency parsers without annotated target corpora and parallel corpora. Previous work either directly applies a discriminative source parser to the target language, …...

32285

InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractNatural language understanding is an important task in modern dialogue systems. It becomes more important with the rapid extension of the dialogue systems’ functionality. In this work, we present an …...

20329

Multilingual and cross-lingual document classification: A meta-learning approach

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractThe great majority of languages in the world are considered under-resourced for successful application of deep learning methods. In this work, we propose a meta-learning approach to document classification in …...

42647

Nearest Neighbour Few-Shot Learning for Cross-lingual Classification

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractEven though large pre-trained multilingual models (e.g. mBERT, XLM-R) have led to significant performance gains on a wide range of cross-lingual NLP tasks, success on many downstream tasks still relies …...

31967

UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractTransfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks. However, annotated data for every target task in every target language is rare, especially for low-resource languages. We …...

8727