MetaXLR -- Mixed Language Meta Representation Transformation for Low-resource Cross-lingual Learning based on Multi-Armed Bandit (2306.00100v1)

Abstract: Transfer learning for extremely low resource languages is a challenging task as there is no large scale monolingual corpora for pre training or sufficient annotated data for fine tuning. We follow the work of MetaXL which suggests using meta learning for transfer learning from a single source language to an extremely low resource one. We propose an enhanced approach which uses multiple source languages chosen in a data driven manner. In addition, we introduce a sample selection strategy for utilizing the languages in training by using a multi armed bandit algorithm. Using both of these improvements we managed to achieve state of the art results on the NER task for the extremely low resource languages while using the same amount of data, making the representations better generalized. Also, due to the method ability to use multiple languages it allows the framework to use much larger amounts of data, while still having superior results over the former MetaXL method even with the same amounts of data.

MetaXLR: Advancements in Low-resource Cross-lingual Learning

This paper presents a significant enhancement in the domain of low-resource language transfer learning, focusing on the Named Entity Recognition (NER) task. The proposed approach, MetaXLR, builds upon previous works like MetaXL by utilizing a novel strategy that leverages multiple source languages and employs a Multi-Armed Bandit (MAB) algorithm for effective sample selection during training. The work seeks to address the limitations encountered with extremely low-resource languages by optimizing the selection and utilization of source languages, thereby improving the generalization of language representations.

Problem Domain

The crux of this research lies in transfer learning for languages that lack sufficient monolingual corpora for pre-training and annotated data for fine-tuning—commonly referred to as low-resource languages. Prior methods, such as the MetaXL framework, relied on a single high-resource language to facilitate learning tasks while also attempting to map those learnings onto low-resource languages. However, this constricted approach doesn't fully exploit the potential generalization offered by multiple source languages.

Methodological Advances

The authors introduce two pivotal advancements over existing methodologies:

1. Multi-Source Language Use: Unlike the traditional single-source language approach, MetaXLR incorporates multiple source languages determined through data-driven strategies. This method leverages clusters of related languages, notably utilizing LangRank for initial language selection and further informed by language clustering insights.
2. Multi-Armed Bandit-Based Sampling: To effectively balance the training contributions from various source languages, the paper proposes using an MAB strategy. By treating each language as an arm of the bandit, the strategy increases the sampling weight of languages that are hard to learn from, thereby facilitating a nuanced and adaptative training process. The MAB employed here is the EXP3 algorithm, which is adept at handling non-stochastic environments.

Experimental Validation

The authors validate their approach using the XLM-R model on the WikiAnn dataset, which spans 282 languages for NER. The results, as shown in the tables provided in the paper, demonstrate a notable improvement across various configurations. Specifically, MetaXLR surpasses other methods by at least 2.4 F1 scores on average, using identical data volumes. The marked improvement highlights the effectiveness of both using related languages and integrating an MAB-based sampling approach.

Implications and Future Directions

The results indicate that the MetaXLR method significantly enhances the performance of NER tasks for extremely low-resource languages, pointing to a potential paradigm shift in how cross-lingual learning challenges are approached. By demonstrating that diverse language representation can be effectively generalized with the right balance and selection strategy, MetaXLR sets the stage for future research to explore further improvements in multilingual NLP applications, particularly in contexts with scarce data availability.

In terms of future development, expanding this approach to other challenging NLP tasks or integrating additional dynamic language selection mechanisms could present valuable enhancement opportunities. Moreover, investigating the scalability of this method with even larger clusters of varying languages remains a promising avenue for broader application.

In conclusion, MetaXLR offers a refined methodology for low-resource cross-lingual learning, showcasing the potential of combining a thoughtful multi-source language strategy with advanced adaptive learning techniques, thereby setting a new benchmark in the domain of multilingual NLP.