Emerging Cross-lingual Structure in Pretrained Language Models (1911.01464v3)

Abstract: We study the problem of multilingual masked LLMing, i.e. the training of a single model on concatenated text from multiple languages, and present a detailed study of several factors that influence why these models are so effective for cross-lingual transfer. We show, contrary to what was previously hypothesized, that transfer is possible even when there is no shared vocabulary across the monolingual corpora and also when the text comes from very different domains. The only requirement is that there are some shared parameters in the top layers of the multi-lingual encoder. To better understand this result, we also show that representations from independently trained models in different languages can be aligned post-hoc quite effectively, strongly suggesting that, much like for non-contextual word embeddings, there are universal latent symmetries in the learned embedding spaces. For multilingual masked LLMing, these symmetries seem to be automatically discovered and aligned during the joint training process.

Cross-lingual Structures in Pretrained LLMs: An Empirical Investigation

The paper of multilingual masked LLMing (MLM) has become paramount as researchers attempt to fine-tune transfer learning for cross-lingual tasks. This paper provides a meticulous empirical analysis of the internal mechanisms that facilitate effective cross-lingual transfer in multilingual LLMs like mBERT and XLM. Through a series of controlled experiments, the paper evaluates the influences of shared vocabulary, domain similarity, parameter sharing, and language relatedness on cross-lingual performance.

The authors deploy a variety of experimental configurations to dissect the factors underpinning the success of cross-lingual transfer in MLMs. The use of BERT-based architectures pretrained on concatenated multilingual corpora is at the core of this work, with novel insights into the surprising efficacy of some configurations over others.

Key Findings

1. Parameter Sharing: The experiments revealed that parameter sharing in the top layers of the multilingual encoder substantially preserves cross-lingual effectiveness, even in the absence of shared vocabularies or domain similarities. The empirical data suggests that commonality in top-layer parameters is fundamental to aligning representations across languages, corroborating the hypothesis that universal latent representations are learned.
2. Shared Vocabulary and Anchor Points: Contrary to prior assertions, the paper found that shared vocabulary (i.e., anchor points) only minimally impacts cross-lingual transfer performance. Even without any shared subwords across languages, strong transfer is possible, emphasizing that shared language embeddings contribute less to cross-lingual efficacy than previously assumed.
3. Domain Similarity: While domain differences between training data impacted cross-lingual performance, the effect was modest compared to the paramount role played by parameter sharing. This suggests that the ability of multilingual models to generalize may rely less on domain similarity than on the structural similarity of representations.
4. Language Similarity: Results showed that related languages benefit more distinctly from cross-lingual pretraining. The transferability improved with language similarity, particularly in complex tasks, which indicates a certain linguistic bias inherent in the model's learned representations.

Implications and Future Directions

The implication of these findings is twofold. Theoretically, this paper supports the proposition that MLMs encode a form of universal language structure, enabling transfer without explicit anchor points. Practically, the findings offer pathways to optimize cross-lingual models—by focusing on parameter sharing—thereby reducing reliance on large shared vocabularies and extensive parallel corpora.

The paper opens avenues for future research in AI to explore the potential of enhancing cross-lingual representations for distant language pairs, potentially incorporating cross-lingual signals or joint training techniques. Moreover, as monolingual models show potential for alignment, investigations into efficient alignment methods—without the requirement for pre-trained models on concurrent data—could bear fruitful outcomes in languages with limited resources.

This paper's contributions significantly advance our understanding of multilingual pretraining dynamics, emboldening research efforts towards more inclusive and generalized AI systems that power cross-lingual applications effectively.