Cross-lingual learning for text processing: A survey

Many intelligent systems in business, government or academy process natural language as an input during inference or they might even communicate with users in natural language. The natural language processing is currently often done with machine learning models. However, machine learning needs training data and such data are often scarce for low-resource languages. The lack of data and resulting poor performance of natural language processing can be solved with cross-lingual learning. Cross-lingual learning is a paradigm for transferring knowledge from one natural language to another. The transfer of knowledge can help us overcome the lack of data in the target languages and create intelligent systems and machine learning models for languages, where it was not possible previously. Despite its increasing popularity and potential, no comprehensive survey on cross-lingual learning was conducted so far. We survey 173 text processing cross-lingual learning papers and examine tasks, data sets and languages that were used. The most important contribution of our work is that we identify and analyze four types of cross-lingual transfer based on what is being transferred. Such insight might help other NLP researchers and practitioners to understand how to use cross-lingual learning for wide range of problems. In addition, we identify what we consider to be the most important research directions that might help the community to focus their future work in cross-lingual learning. We present a comprehensive table of all the surveyed papers with various data related to the cross-lingual learning techniques they use. The table can be used to find relevant papers and compare the approaches to cross-lingual learning. To the best of our knowledge, no survey of cross-lingual text processing techniques was done in this scope before. (C) 2020 Published by Elsevier Ltd.

Automated summary

This study surveys 173 cross-lingual learning papers in text processing, examining tasks, data sets, and languages used. The key contribution is the identification and analysis of four types of cross-lingual transfer based on what is being transferred, which can assist NLP researchers in understanding how to apply cross-lingual learning to various problems. Additionally, important research directions are highlighted to guide future work in cross-lingual learning, aiming to provide a comprehensive overview of the field.