Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks (1909.00964v2)

Abstract: We present Unicoder, a universal language encoder that is insensitive to different languages. Given an arbitrary NLP task, a model can be trained with Unicoder using training data in one language and directly applied to inputs of the same task in other languages. Comparing to similar efforts such as Multilingual BERT and XLM, three new cross-lingual pre-training tasks are proposed, including cross-lingual word recovery, cross-lingual paraphrase classification and cross-lingual masked LLM. These tasks help Unicoder learn the mappings among different languages from more perspectives. We also find that doing fine-tuning on multiple languages together can bring further improvement. Experiments are performed on two tasks: cross-lingual natural language inference (XNLI) and cross-lingual question answering (XQA), where XLM is our baseline. On XNLI, 1.8% averaged accuracy improvement (on 15 languages) is obtained. On XQA, which is a new cross-lingual dataset built by us, 5.5% averaged accuracy improvement (on French and German) is obtained.

Unicoder: Advancements in Universal Cross-lingual Language Encoding

The paper presents Unicoder, an advanced universal language encoder designed for cross-lingual NLP tasks. Developed by Microsoft Research Asia and affiliated groups, the Unicoder model demonstrates superior performance in transferring knowledge across languages, enabling a model trained in one language to be effectively applied to another. This is achieved by the novel integration of multiple cross-lingual pre-training tasks beyond those utilized by previous models such as Multilingual BERT and XLM.

Core Innovations

Unicoder introduces three distinct cross-lingual pre-training tasks:

1. Cross-lingual Word Recovery: This task leverages the attention matrix between bilingual sentence pairs to establish cross-lingual word alignments. It allows for efficient learning of relationships between words across different languages.
2. Cross-lingual Paraphrase Classification: Focused on sentence-level alignment, this task determines if two sentences from different languages have equivalent meanings. It enhances the model's ability to understand semantic parity across linguistic boundaries.
3. Cross-lingual Masked LLM (MLM): Unlike traditional monolingual pre-training on documents, this task aggregates documents written in multiple languages, utilizing similar techniques to those proven effective in models like BERT and GPT.

Experimental Evaluation

The performance of Unicoder was tested on two challenging tasks: Cross-lingual Natural Language Inference (XNLI) and a newly constructed Cross-lingual Question Answering (XQA) dataset. In XNLI, Unicoder achieved an average accuracy improvement of 1.8% over the state-of-the-art XLM when using machine-translated training data across multiple languages. On the XQA dataset, Unicoder outperformed XLM by 5.5%, highlighting its efficiency in cross-lingual understanding and response generation.

Implications and Future Directions

Unicoder's development adds significant value to the field of multilingual NLP. By showcasing that pre-training with multiple, diverse linguistic tasks can lead to enhanced language-agnostic models, this research pushes the boundaries of what pre-trained models can achieve. The strong cross-lingual performance of Unicoder suggests potential practical applications in global communication systems, including automatic translation and multilingual digital assistants.

From a theoretical perspective, Unicoder paves the way for further exploration into multi-faceted pre-training frameworks. Future research could investigate the effectiveness of integrating additional tasks or optimizing task selection and weighting to foster even greater model generalization across languages. Moreover, scalable fine-tuning strategies, such as Multi-language Fine-tuning employed in Unicoder, could be expanded or refined, offering new insights into efficient model adaptation for diverse linguistic landscapes.

Conclusion

Unicoder embodies a significant advancement in cross-lingual language representation learning, validated by empirical improvements in distinct NLP benchmarks. Its contribution to the ongoing effort to build more effective and versatile LLMs positions it as a pivotal development in the NLP research community, with far-reaching implications for enhancing global linguistic technologies.