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Geometric Representational Disparities in Multilingual and Bilingual Translation Models


Core Concepts
Multilingual decoder representations have reduced isotropy compared to bilingual models, impacting performance.
Abstract
Multilingual machine translation offers parameter efficiency and language transfer. Some language pairs in multilingual models perform worse than in bilingual models. Capacity limitations contribute to reduced performance in multilingual models. Isotropy metrics reveal differences in representation utilization between bilingual and multilingual models. Multilingual encoder capacity slightly increases, while decoder capacity significantly decreases. Larger training scales result in reduced space utilization in both multilingual and bilingual models. Multiparallel data shows improved encoder isotropy but mixed results for decoder isotropy. Multilingual decoders exhibit language-specific representation clusters, impacting overall isotropy. Layerwise analysis shows increasing language specificity in decoder representations.
Stats
"For a given language pair, its multilingual model decoder representations are consistently less isotropic and occupy fewer dimensions than comparable bilingual model decoder representations." "Source-side representation capacity improves slightly in one-to-many models over bilingual models." "Reduced space utilization in multilingual decoder representations seems driven by language-specific information occupying much of the available representation space."
Quotes
"Multilingual machine translation has proven immensely useful for both parameter efficiency and overall performance." "Some language pairs in multilingual models can see worse performance than in bilingual models." "Previous work has hypothesized that limited modeling capacity is a major contributor to reduced performance in multilingual models."

Deeper Inquiries

What alternative approaches can be considered to address the reduced representational capacity in multilingual decoders

One alternative approach to address the reduced representational capacity in multilingual decoders is to implement partial sharing between language decoders. By allowing certain parameters to be shared across languages while keeping others language-specific, the model can strike a balance between leveraging shared knowledge and preserving language-specific information. This approach, as proposed by Sachan and Neubig (2018), can help reduce interference and improve performance in multilingual translation models. Additionally, techniques like knowledge distillation, mix of language-specific and language-agnostic parameters, and gradient gating methods can also be explored to mitigate the impact of reduced representational capacity in multilingual decoders.

How might the findings of increased encoder isotropy and decreased decoder isotropy impact the design of future multilingual translation models

The findings of increased encoder isotropy and decreased decoder isotropy can significantly impact the design of future multilingual translation models. Increased encoder isotropy suggests that multilingual encoder spaces benefit from sharing across languages, indicating a potential for improved source-side representations in multilingual models. On the other hand, decreased decoder isotropy highlights the challenge of language separation in decoder representations. Future models could leverage these findings by incorporating mechanisms to balance language-specific information in decoders while maintaining shared knowledge across languages. This could lead to more efficient and effective multilingual translation systems with optimized representational capacity in both encoder and decoder components.

How can the phenomenon of increasing language specificity in decoder representations be leveraged for improved translation performance

The phenomenon of increasing language specificity in decoder representations can be leveraged for improved translation performance by fine-tuning the model to better capture language-specific nuances. By allowing decoder representations to become more language-specific as they progress through layers, the model can better handle the intricacies of each target language. This can lead to enhanced translation quality, especially in capturing language-specific nuances, idiomatic expressions, and cultural references. Leveraging this language-specificity in decoder representations can help improve the overall fluency and accuracy of translations in multilingual models.
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