A Survey Of Cross-lingual Word Embedding Models

Abstract: Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages. In this survey, we provide a comprehensive typology of cross-lingual word embedding models. We compare their data requirements and objective functions. The recurring theme of the survey is that many of the models presented in the literature optimize for the same objectives, and that seemingly different models are often equivalent modulo optimization strategies, hyper-parameters, and such. We also discuss the different ways cross-lingual word embeddings are evaluated, as well as future challenges and research horizons.

• The paper introduces a typology of embedding models based on word, sentence, and document alignment data.
• It demonstrates that models leveraging richer alignments, such as word and sentence levels, achieve superior performance on fine-grained tasks.
• The study advocates for improved data quality and robust unsupervised methods to drive future multilingual NLP advancements.

A Survey of Cross-lingual Word Embedding Models

Cross-lingual word embeddings have emerged as a pivotal tool for advancing multilingual NLP by enabling transfer of lexical knowledge across languages. The paper "A Survey of Cross-lingual Word Embedding Models" provides a comprehensive review of existing models in this domain. The survey is meticulously organized around a typology based on the data requirements and alignment levels of the methods, offering insights into their similarities and distinctive characteristics.

Typology and Data Requirements

The survey classifies cross-lingual word embedding models based on the type and level of data alignment they utilize:

1. Word Alignment: Models using word-level data typically employ bilingual dictionaries or automatic word alignments from parallel corpora. These can be further divided into mapping-based methods, pseudo-bilingual corpora approaches, and joint models.
2. Sentence Alignment: Sentence-level models leverage parallel corpora, often extending monolingual embedding techniques to the bilingual setting. These include compositional model adaptations, autoencoder-based approaches, and skip-gram adaptations for sentence pairs.
3. Document Alignment: Document-level approaches utilize aligned or comparable documents, often through pseudo-bilingual documents or topic model-based methods.

Each approach carries strengths depending on the richness of the data available and the particular linguistic challenges posed by the languages involved.

Numerical Results and Claims

The survey emphasizes the impact of data type over algorithmic innovations in determining model performance. It highlights that models which leverage sentence and word-level alignments generally outperform those relying merely on comparable documents, particularly for fine-grained tasks like bilingual dictionary induction.

Implications and Future Directions

The theoretical implications are profound, suggesting that the direction of future research should focus on improving data quality and exploiting richer and diverse alignment signals. Unsupervised models are gaining traction, though challenges remain in ensuring robustness, particularly for language pairs with fewer shared cognates or structural isomorphisms.

Practically, cross-lingual embeddings have the potential to revolutionize applications such as machine translation, cross-lingual information retrieval, and multilingual semantic understanding. Moving forward, research should address challenges such as handling polysemy, non-linear mapping capabilities, and the integration of subword information to bolster performance in morphologically rich languages.

Conclusion

This survey is a valuable resource for researchers aiming to understand the landscape of cross-lingual word embeddings. By systematically categorizing existing models and drawing correlations between their data requirements and performance, it sets a stage for future advancements in multilingual NLP. As the field progresses, addressing current challenges and broadening data exploitation techniques could lead to significant improvements in cross-lingual language understanding.