When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation? (1804.06323v2)

Abstract: The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases -- providing gains of up to 20 BLEU points in the most favorable setting.

• The paper demonstrates that pre-trained embeddings significantly boost NMT performance in low-resource scenarios, achieving gains of up to 20 BLEU points.
• The paper finds that embedding benefits vary with language resource levels and similarity, with high-resource languages improving by around 3 BLEU points and low-resource cases showing more variability.
• The paper reveals that while explicit alignment of embedding spaces is unnecessary in bilingual settings, it can enhance performance in multilingual systems.

Analyzing the Usefulness of Pre-trained Word Embeddings in Neural Machine Translation

The paper "When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?" by Ye Qi et al., investigates the impact of pre-trained word embeddings on Neural Machine Translation (NMT) systems, particularly in low-resource scenarios. The research addresses a significant gap in understanding the conditions under which pre-trained embeddings improve NMT performance and explores the mechanisms behind observed performance gains.

Overview and Methodology

The authors performed a series of experiments to assess the utility of pre-trained embeddings across multiple dimensions. The experimental setup included a diverse multilingual corpus derived from TED talks, focusing on language pairs with varying degrees of linguistic similarity and resource availability. These experiments sought to answer five key questions:

1. Linguistic Family Influence: Does the efficacy of pre-training depend on the linguistic characteristics of the source and target languages?
2. Effect of Training Data Size: How does the available training data size affect the usefulness of pre-trained embeddings?
3. Language Similarity: How does linguistic similarity between source and target languages impact performance gains from pre-trained embeddings?
4. Alignment of Embedding Spaces: Is it beneficial to align the embedding spaces between different languages?
5. Multilingual Contexts: Do pre-trained embeddings have increased utility in multilingual translation systems compared to bilingual systems?

The NMT model used for these experiments was a standard encoder-decoder architecture with attention, evaluated using the BLEU metric. The models were initialized with pre-trained embeddings using fastText and were compared against systems with randomly initialized embeddings.

Key Findings

1. Performance in Low-resource Scenarios: Pre-trained embeddings provide substantial improvements in low-resource scenarios, with performance gains of up to 20 BLEU points in the most favorable settings. Notably, the largest improvements occur when the baseline NMT system achieves moderate initial performance, suggesting embeddings are most beneficial when models are on the cusp of effective translation.
2. Impact of Language Characteristics: The impact of pre-trained embeddings varies depending on the source and target language pair. Higher resource languages show consistent improvements (approximately 3 BLEU points), while very low-resource languages exhibit variable gains, highlighting the role of pre-trained embeddings in elevating baseline systems to functional performance levels.
3. Influence of Linguistic Similarity: The paper hypothesizes that similar linguistic structures between languages might allow for better transfer of semantic information when using pre-trained embeddings. However, results indicate mixed outcomes, with the largest gains not always correlating with linguistic similarity.
4. Embedding Space Alignment: Contrary to initial expectations, aligning embedding spaces across source and target languages did not consistently improve performance in bilingual settings. This suggests that NMT systems are capable of internally learning effective projections without explicit alignment. However, in multilingual contexts, embedding alignment shows potential benefits, facilitating shared learning across similar languages.
5. Multilingual Systems: In multilingual configurations, pre-trained embeddings prove beneficial, especially for closely related language pairs. For language pairs with less linguistic similarity, such as Belarusian and Russian, the gains are more modest, possibly due to rich morphological features increasing sparsity in embeddings.

Implications and Future Directions

This research offers important practical insights for NMT in low-resource settings. It recommends employing pre-trained embeddings when data scarcity hinders system performance. The findings underscore the potential of embeddings for enhancing multilingual systems and suggest further exploration into alternative methods of embedding alignment and representation sharing.

Future work may build on these findings by exploring dynamic pre-training methods that adjust embeddings based on the evolving needs of low- and multi-resource translation tasks. Additionally, integrating these insights with newer transformer-based architectures could provide further advances in NMT performance.

By clarifying when and why pre-trained word embeddings are useful for NMT, this paper contributes significantly to the optimization of translation systems in challenging multilingual environments.