Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing (1902.09492v2)

Abstract: We introduce a novel method for multilingual transfer that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. While contextual embeddings have been shown to yield richer representations of meaning compared to their static counterparts, aligning them poses a challenge due to their dynamic nature. To this end, we construct context-independent variants of the original monolingual spaces and utilize their mapping to derive an alignment for the context-dependent spaces. This mapping readily supports processing of a target language, improving transfer by context-aware embeddings. Our experimental results demonstrate the effectiveness of this approach for zero-shot and few-shot learning of dependency parsing. Specifically, our method consistently outperforms the previous state-of-the-art on 6 tested languages, yielding an improvement of 6.8 LAS points on average.

Analysis of Cross-Lingual Alignment of Contextual Word Embeddings for Dependency Parsing

The paper "Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-Shot Dependency Parsing" introduces a novel method for leveraging deep contextual embeddings in multilingual NLP tasks. This method is distinguished by its approach to aligning contextual embeddings across languages, thereby facilitating improved performance in zero-shot and few-shot dependency parsing. The authors utilize the ELMo model to generate embeddings and propose alignment strategies that extend beyond static word embeddings.

Contextual embeddings, due to their dynamic nature adjusted per-context, provide a more nuanced semantic and syntactic representation than traditional embeddings. However, aligning these embeddings poses significant challenges. The authors address this by creating context-independent variants, also referred to as "anchors," and using these to guide the alignment of context-dependent spaces.

Methodology

The paper differentiates between supervised and unsupervised methods for alignment.

• Supervised Alignment: This approach utilizes a bilingual dictionary to map context-independent anchors across languages. The alignment matrix is optimized using orthogonal Procrustes analysis, which ensures inter-lingual geometric relations are preserved during the transformation.
• Unsupervised Alignment: Extending on the MUSE framework, the authors describe an adversarial training setup to learn the alignment without supervision. An iterative refinement process is employed, improving upon the synthesized dictionary created during initial alignment steps.

In both methods, emphasis is placed on the utility of context-independent anchors. The experiments demonstrate that such anchors can simplify the mapping problem while maintaining representation detail important for downstream tasks.

Empirical Evaluation

The evaluation benchmarks the proposed approach predominantly on cross-lingual dependency parsing tasks, emphasizing zero-shot settings where a model trained on one language treebank is applied to another without exposure to the target language's annotations.

• Zero-Shot Multilingual Parsing: Through extensive experiments across multiple languages, the authors report an average improvement of 6.8 LAS (Labeled Attachment Score) points over prior state-of-the-art methods. Notably, their method, even when unsupervised (and not reliant on POS tags), performs comparably to or outperforms methods that utilize these resources.
• Few-Shot Learning: On particularly challenging tasks with limited annotated data (e.g., languages with minimal treebank sizes), the model showcases its robustness. For the Kazakh dataset, the proposed method achieves an impressive gain over state-of-the-art approaches from shared task benchmarks.

The authors further conduct experiments with limited amounts of unannotated data to test the resilience of their method under constrained scenarios. Similarly, they explore scenarios void of bilingual dictionaries, showcasing consistent performance gains with their unsupervised anchor methodology.

Implications and Future Directions

The findings have significant implications for multilingual NLP and resource-poor languages. By providing a framework that efficiently aligns contextual embeddings, the method lays groundwork for more effective deployment of NLP systems in languages with limited resources. It proposes practical steps towards the operationalization of modern NLP architectures in low-resource environments, without the traditionally heavy reliance on language-specific annotations or external linguistic resources like bilingual dictionaries.

Future directions might explore extending these techniques to other pre-trained contextual models (e.g., BERT or GPT) and expanding the applicability beyond dependency parsing to other syntactic tasks where cross-lingual transfer remains a challenge. Additionally, experimenting with diverse linguistic families and further refining the unsupervised setup could yield further insights into the dynamics of cross-lingual embedding spaces.

In summary, the paper contributes a meaningful advancement in the area of cross-lingual NLP, presenting both theoretical and empirical validation for its proposed alignment strategies, achieving considerable performance improvements in challenging parsing scenarios.