insikt - Computational Complexity - # Cross-lingual Transfer for CJKV Languages

CORI: A Comprehensive Benchmark for Cross-lingual Transfer in Chinese-Japanese-Korean-Vietnamese Languages

Q: How can the proposed framework be extended to handle more diverse language pairs beyond the CJKV group, such as languages with different writing systems (e.g., Arabic, Cyrillic)?

The proposed framework for integrating orthographic and phonemic representations can be extended to handle more diverse language pairs by adapting the pre-processing steps and augmentation techniques to accommodate languages with different writing systems. Here are some ways to extend the framework: Adapting Pre-processing: For languages with different writing systems like Arabic or Cyrillic, the pre-processing step would involve segmenting the text into meaningful units based on the specific characteristics of those languages. This may require language-specific tokenization and segmentation techniques to ensure the alignment of orthographic and phonemic representations. Incorporating Language-specific Tools: Utilize language-specific tools for pre-segmentation and Romanization to handle the unique features of languages with different writing systems. This may involve using Arabic script transliteration tools for Arabic languages or Cyrillic transliteration tools for languages using the Cyrillic script. Augmentation Strategies: Develop code-switching augmentation techniques tailored to languages with different writing systems. This may involve generating parallel text in both the original script and Romanized form for languages with non-Latin scripts to create multi-view representations for cross-lingual transfer. Fine-tuning for Diverse Languages: Fine-tune the model on a diverse set of language pairs with varying writing systems to capture the nuances of different languages. This would involve training the model on a wide range of language pairs to improve its ability to handle diverse linguistic characteristics. By adapting the pre-processing steps, incorporating language-specific tools, implementing tailored augmentation strategies, and fine-tuning the model on diverse language pairs, the proposed framework can be extended to effectively handle more diverse language pairs beyond the CJKV group.

Q: How can the CORI benchmark be further expanded to include domain-specific datasets and tasks to better reflect real-world cross-lingual applications?

Expanding the CORI benchmark to include domain-specific datasets and tasks can enhance its relevance to real-world cross-lingual applications. Here are some strategies to further expand the CORI benchmark: Domain-specific Task Integration: Identify specific domains such as legal, medical, or financial domains that require cross-lingual applications. Integrate domain-specific datasets and tasks related to these domains into the CORI benchmark to evaluate model performance in domain-specific contexts. Task Diversity: Include a diverse range of tasks beyond traditional NLU tasks, such as sentiment analysis, document classification, or named entity recognition in specific domains. This will provide a comprehensive evaluation of cross-lingual models across various real-world applications. Multimodal Data: Incorporate multimodal datasets that combine text with other modalities like images or audio. This will enable the evaluation of cross-lingual models in scenarios where language is integrated with other forms of data. Fine-grained Evaluation: Develop fine-grained evaluation metrics that capture the nuances of domain-specific tasks. This could involve task-specific evaluation criteria that reflect the performance requirements of real-world applications. Collaboration with Domain Experts: Collaborate with domain experts in specific fields to curate relevant datasets and design tasks that mirror real-world challenges. This collaboration will ensure that the benchmark reflects the complexities of cross-lingual applications in diverse domains. By expanding the CORI benchmark to include domain-specific datasets, tasks, and evaluation metrics, researchers and practitioners can better assess the performance of cross-lingual models in real-world applications and domains.

Centrala begrepp

Careful selection of source language and integration of phonemic information beyond orthographic scripts can significantly enhance cross-lingual transfer performance for CJKV languages.

Sammanfattning

The paper presents a comprehensive study on the impact of source language selection and the importance of capturing phonemic information beyond orthographic scripts for cross-lingual transfer among Chinese-Japanese-Korean-Vietnamese (CJKV) languages.

The key highlights are:

A preliminary study demonstrates that using Chinese (ZH) as the source language leads to significantly better zero-shot cross-lingual transfer performance on target CJKV languages compared to using English (EN) as the source.
The authors construct a novel benchmark dataset called CORI that covers diverse NLU tasks for CJKV languages. CORI addresses limitations in the existing XTREME benchmark, such as inconsistent pre-segmentation across languages and lack of phonemic information.
The authors propose a simple framework that integrates orthographic and phonemic (Romanized) representations via contrastive learning objectives. This leads to enhanced cross-lingual representations and improved downstream task performance on CJKV languages.
Extensive experiments show that the proposed approach outperforms various state-of-the-art cross-lingual transfer methods on the CORI benchmark, demonstrating the importance of careful source language selection and integration of phonemic information.

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Statistik

The average performance of target JKV languages increases by 11.21% accuracy on XNLI, 10.40 F1 on PANX, and 4.08 EM on MLQA when using the CORI dataset compared to the raw XTREME dataset.
The Centered Kernel Alignment (CKA) score between ZH (source) and VI (target) representations improves from 0.0012 to 0.2024 when Romanized transcriptions are integrated, indicating better cross-lingual alignment.

Citat

"Naively assuming English as a source language may hinder cross-lingual transfer for many languages by failing to consider the importance of language contact."
"As language acquisition naturally benefits from various linguistic modalities, including the textual writing scripts and aural signals, which can be conveyed through orthographic and phonemic transcription respectively, the aforementioned constraint hinders progress in the cross-lingual field, by minimizing the amount of relevant information that models may be able to access."

Viktiga insikter från

CORI: CJKV Benchmark with Romanization Integration -- A step towards Cross-lingual Transfer Beyond Textual Scripts

by Hoang H. Ngu... på arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12618.pdf

CORI: CJKV Benchmark with Romanization Integration -- A step towards Cross-lingual Transfer Beyond Textual Scripts

Djupare frågor

How can the proposed framework be extended to handle more diverse language pairs beyond the CJKV group, such as languages with different writing systems (e.g., Arabic, Cyrillic)?

The proposed framework for integrating orthographic and phonemic representations can be extended to handle more diverse language pairs by adapting the pre-processing steps and augmentation techniques to accommodate languages with different writing systems. Here are some ways to extend the framework:

Adapting Pre-processing: For languages with different writing systems like Arabic or Cyrillic, the pre-processing step would involve segmenting the text into meaningful units based on the specific characteristics of those languages. This may require language-specific tokenization and segmentation techniques to ensure the alignment of orthographic and phonemic representations.

Incorporating Language-specific Tools: Utilize language-specific tools for pre-segmentation and Romanization to handle the unique features of languages with different writing systems. This may involve using Arabic script transliteration tools for Arabic languages or Cyrillic transliteration tools for languages using the Cyrillic script.

Augmentation Strategies: Develop code-switching augmentation techniques tailored to languages with different writing systems. This may involve generating parallel text in both the original script and Romanized form for languages with non-Latin scripts to create multi-view representations for cross-lingual transfer.

Fine-tuning for Diverse Languages: Fine-tune the model on a diverse set of language pairs with varying writing systems to capture the nuances of different languages. This would involve training the model on a wide range of language pairs to improve its ability to handle diverse linguistic characteristics.

By adapting the pre-processing steps, incorporating language-specific tools, implementing tailored augmentation strategies, and fine-tuning the model on diverse language pairs, the proposed framework can be extended to effectively handle more diverse language pairs beyond the CJKV group.

How can the CORI benchmark be further expanded to include domain-specific datasets and tasks to better reflect real-world cross-lingual applications?

Expanding the CORI benchmark to include domain-specific datasets and tasks can enhance its relevance to real-world cross-lingual applications. Here are some strategies to further expand the CORI benchmark:

Domain-specific Task Integration: Identify specific domains such as legal, medical, or financial domains that require cross-lingual applications. Integrate domain-specific datasets and tasks related to these domains into the CORI benchmark to evaluate model performance in domain-specific contexts.

Task Diversity: Include a diverse range of tasks beyond traditional NLU tasks, such as sentiment analysis, document classification, or named entity recognition in specific domains. This will provide a comprehensive evaluation of cross-lingual models across various real-world applications.

Multimodal Data: Incorporate multimodal datasets that combine text with other modalities like images or audio. This will enable the evaluation of cross-lingual models in scenarios where language is integrated with other forms of data.

Fine-grained Evaluation: Develop fine-grained evaluation metrics that capture the nuances of domain-specific tasks. This could involve task-specific evaluation criteria that reflect the performance requirements of real-world applications.

Collaboration with Domain Experts: Collaborate with domain experts in specific fields to curate relevant datasets and design tasks that mirror real-world challenges. This collaboration will ensure that the benchmark reflects the complexities of cross-lingual applications in diverse domains.

By expanding the CORI benchmark to include domain-specific datasets, tasks, and evaluation metrics, researchers and practitioners can better assess the performance of cross-lingual models in real-world applications and domains.

What other techniques beyond contrastive learning could be explored to effectively integrate orthographic and phonemic representations for cross-lingual transfer?

In addition to contrastive learning, several other techniques can be explored to effectively integrate orthographic and phonemic representations for cross-lingual transfer. Here are some alternative approaches:

Adversarial Training: Adversarial training can be used to align orthographic and phonemic representations by introducing a discriminator that distinguishes between the two modalities. By training the model to generate representations that fool the discriminator, the model can learn to effectively integrate both types of representations.

Multi-task Learning: Multi-task learning can be employed to simultaneously train the model on tasks that require both orthographic and phonemic understanding. By sharing parameters across tasks, the model can learn to leverage both types of representations for improved performance on cross-lingual tasks.

Knowledge Distillation: Knowledge distillation involves transferring knowledge from a larger, pre-trained model to a smaller model. By distilling the knowledge of a model trained on both orthographic and phonemic representations into a smaller model, the smaller model can benefit from the integrated knowledge for cross-lingual transfer.

Graph-based Models: Graph-based models can capture the relationships between orthographic and phonemic units in a structured way. By representing the language as a graph where nodes correspond to tokens and edges capture relationships, the model can effectively integrate both types of representations for cross-lingual tasks.

Ensemble Methods: Ensemble methods can combine multiple models trained on orthographic and phonemic representations to make collective predictions. By aggregating the outputs of diverse models, the ensemble can leverage the strengths of each representation type for improved cross-lingual transfer performance.

By exploring these alternative techniques in addition to contrastive learning, researchers can enhance the integration of orthographic and phonemic representations for cross-lingual transfer tasks.