Multi-Source Cross-Lingual Model Transfer: Learning What to Share

Citations Over TimeTop 10% of 2019 papers

Abstract

Modern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of annotated training data for various NLP tasks. Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language by leveraging labeled data from other (source) languages. In this work, we focus on the multilingual transfer setting where training data in multiple source languages is leveraged to further boost target language performance. Unlike most existing methods that rely only on language-invariant features for CLTL, our approach coherently utilizes both languageinvariant and language-specific features at instance level. Our model leverages adversarial networks to learn language-invariant features, and mixture-of-experts models to dynamically exploit the similarity between the target language and each individual source language 1 . This enables our model to learn effectively what to share between various languages in the multilingual setup. Moreover, when coupled with unsupervised multilingual embeddings, our model can operate in a zero-resource setting where neither target language training data nor cross-lingual resources are available. Our model achieves significant performance gains over prior art, as shown in an extensive set of experiments over multiple text classification and sequence tagging tasks including a large-scale industry dataset.

Related Papers

• → Language Model Adaptation For Statistical Machine Translation Based On Information Retrieval(2004)77 cited
• → Random forests and the data sparseness problem in language modeling(2006)62 cited
• Code-Switch Language Model with Inversion Constraints for Mixed Language Speech Recognition(2012)
• → Improving N-gram language modeling for code-switching speech recognition(2017)16 cited
• → An empirical study of statistical language models: n-gram language models vs. neural network language models(2018)10 cited