Comparison of Cross-lingual Transfer and Machine Translation for Monolingual Semantic Textual Similarity

Core Concepts

The author compares cross-lingual transfer and machine translation for monolingual STS, finding comparable performance. They highlight the effectiveness of Wikipedia data in improving STS results.

Abstract

The study evaluates two data augmentation techniques, cross-lingual transfer, and machine translation, for monolingual semantic textual similarity (STS). The comparison is conducted on Japanese and Korean languages, known for their linguistic dissimilarity to English. The research aims to find the most suitable technique for monolingual STS by addressing specific research questions. The findings suggest that both techniques yield similar performance levels in monolingual STS tasks. Surprisingly, the study reveals that the cross-lingual transfer of Wikipedia data outperforms machine translation in certain scenarios. This indicates the potential of using native Wikipedia data as an effective training resource for improving sentence embeddings.

Stats

In our experiments on monolingual STS in Japanese and Korean, we find that the two data techniques yield performance on par.
Specifically, we trained mSimCSE models using English training data alone as cross-lingual transfer and machine-translated data from English into Korean and Japanese.
We demonstrate that cross-lingual transfer achieves performance on par with that of machine translation.
The mSimCSEen model trained using Wikipedia data outperformed its counterpart using NLI data.
The cross-lingual transfer of Wikpedia outperformed the machine translation of NLI, resulting in performance almost outperforming that of LaBSE.

Quotes

"Learning better sentence embeddings leads to improved performance for natural language understanding tasks." - Abstract
"We found a superiority of the Wikipedia domain over the NLI domain for these languages." - Content

Key Insights Distilled From

Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity

by Sho Hoshino,... at arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05257.pdf

Deeper Inquiries

Should native multilingual data be prioritized over machine-translated datasets?

In the context of cross-lingual transfer and machine translation for monolingual semantic textual similarity (STS), the choice between native multilingual data and machine-translated datasets depends on various factors. Native multilingual data, such as Wikipedia in different languages, can offer more accurate representations of language nuances and semantics compared to machine-translated datasets. Prioritizing native multilingual data can lead to better performance in tasks like STS by capturing the intricacies of each language more effectively.
When considering whether to prioritize native multilingual data over machine-translated datasets, it is essential to evaluate the quality and reliability of the translations provided by machine translation systems. Machine translations may introduce errors or inaccuracies that could impact the overall performance of models trained on such data. Additionally, linguistic differences between languages may not be fully captured through automated translation processes, leading to suboptimal results in downstream tasks like STS.
In conclusion, while both native multilingual data and machine-translated datasets have their advantages and limitations, prioritizing native multilingual data can potentially result in more robust and accurate models for tasks like STS.

Is there a risk of bias or inaccuracies when relying heavily on English as a proxy training language?

Relying heavily on English as a proxy training language for cross-lingual transfer poses certain risks related to bias and inaccuracies in model performance across different languages. When using English as a primary source for training models intended for other languages, several challenges arise:

Linguistic Bias: English-centric training can introduce biases inherent in English text into models transferred to other languages. This bias may affect how well the model generalizes across diverse linguistic contexts.

Cultural Nuances: Languages carry cultural nuances that are unique to specific regions or communities. By solely relying on English as a proxy training language, these cultural subtleties may be overlooked or misrepresented in other languages.

Translation Quality: Machine translation quality varies across different language pairs. Relying solely on translated versions of English text may result in inaccuracies or loss of meaning during the translation process.

To mitigate these risks when using English as a proxy training language, it is crucial to:

Validate model performance across multiple languages independently.
Incorporate diverse linguistic resources beyond just translated versions from English.
Conduct thorough evaluation and validation processes specific to each target language.
By addressing these considerations, one can reduce potential biases and inaccuracies introduced by heavy reliance on English as a primary source for cross-lingual transfer models.

How can leveraging domain-specific datasets impact the effectiveness of different data augmentation techniques?

Leveraging domain-specific datasets plays a significant role in enhancing the effectiveness of various data augmentation techniques like cross-lingual transfer and machine translation for tasks such as semantic textual similarity (STS). The impact of domain-specific datasets includes:

Improved Relevance: Domain-specific datasets provide specialized vocabulary and context relevant to particular industries or topics. By incorporating such domain knowledge into training data augmentation techniques, models can better capture nuanced relationships within that domain.

Enhanced Performance: Training with domain-specific datasets allows models to learn task-specific patterns more effectively than generic corpora would permit. This leads to improved performance metrics when applied back onto similar domains during inference.

3Reduced Generalization Error: Models trained with domain-specific datasets are less likely to suffer from generalization errors when deployed within those domains due to increased exposure during training sessions.
Therefore,
leveraging
domain-
specific
datasets
can significantly enhance
the
effectiveness
of
data
augmentation techniques by providing targeted information tailored towards specific areas
such
as industry jargon,
technical terminology,
or specialized concepts
This approach ensures that augmented datsets closely mirror real-world scenarios encountered within distinct domains,resultinginmoreaccuratemodelsandimprovedperformancemetricsacrossvariousNLPtasksincludingsemantictextsimilarity