Información - Cross-lingual Natural Language Processing - # Cross-lingual structured prediction

Contextual Label Projection for Improving Cross-Lingual Structured Prediction

Q: How can CLaP be extended to other structured prediction tasks beyond EAE and NER?

CLaP can be extended to other structured prediction tasks by following a similar approach of contextual label projection using instruction-tuned language models. The key steps to extend CLaP to other tasks include: Task-specific Label Definitions: Define the specific labels and roles for the new structured prediction task. This step is crucial to ensure accurate translation of the labels in the target language. Data Preprocessing: Preprocess the training data for the new task, including sentence-label pairs in the source language. Model Adaptation: Fine-tune the instruction-tuned language model on the new task-specific data to adapt it to the nuances and requirements of the task. Contextual Translation: Utilize the fine-tuned model to perform contextual translation of the labels based on the translated input sentences, ensuring accuracy and faithfulness in the label projections. Evaluation and Iteration: Evaluate the performance of CLaP on the new task, fine-tune as necessary based on the results, and iterate to improve the model's effectiveness for the specific structured prediction task. By following these steps and customizing CLaP for the requirements of the new structured prediction task, it can be effectively extended beyond EAE and NER to a wide range of tasks.

Q: How can the potential limitations of using instruction-tuned language models for contextual translation be addressed?

While instruction-tuned language models offer benefits for contextual translation in label projection tasks, they also come with potential limitations that need to be addressed: Limited Language Coverage: Instruction-tuned models may not have equal proficiency in all languages, leading to varying translation quality across languages. This limitation can be addressed by training the model on more diverse and representative data to improve language coverage. Bias and Harmful Content: Since instruction-tuned models are not trained on filtered safe content data, there is a risk of generating biased or harmful content. To address this, additional ethical considerations and bias mitigation techniques should be implemented during model training and deployment. Model Interpretability: Instruction-tuned models may lack interpretability, making it challenging to understand the reasoning behind their translations. Addressing this limitation involves incorporating explainability techniques to enhance model transparency and trustworthiness. Resource Intensiveness: Training and fine-tuning instruction-tuned models can be resource-intensive. To address this, efficient training strategies, model compression techniques, and optimization methods can be employed to reduce computational costs. By actively addressing these potential limitations through careful model development, training, and deployment practices, the effectiveness and ethical considerations of using instruction-tuned language models for contextual translation can be improved.

Q: How can the performance of CLaP be further improved for extremely low-resource languages by leveraging additional techniques beyond larger language models?

To enhance the performance of CLaP for extremely low-resource languages, several additional techniques can be leveraged beyond larger language models: Data Augmentation: Implement data augmentation techniques such as back-translation, synthetic data generation, and data synthesis to increase the diversity and quantity of training data for low-resource languages. Transfer Learning: Utilize transfer learning approaches by pre-training CLaP on related tasks or languages with more data and then fine-tuning it on the low-resource languages. This helps in transferring knowledge and improving performance. Multimodal Fusion: Incorporate multimodal data sources such as images, audio, or video along with text data to provide additional context and improve the accuracy of label projections in low-resource languages. Active Learning: Implement active learning strategies to intelligently select and annotate the most informative data points for training CLaP, optimizing the model's performance with limited labeled data. Domain Adaptation: Perform domain adaptation by fine-tuning CLaP on domain-specific data from the low-resource languages to enhance its performance in specific domains or contexts. By combining these additional techniques with CLaP and customizing them for extremely low-resource languages, the model's performance can be further improved, enabling effective cross-lingual structured prediction in challenging linguistic scenarios.

Conceptos Básicos

Contextual Label Projection (CLaP) is a novel approach that leverages contextual machine translation to accurately translate labels while preserving their association with the translated input text, leading to improved performance in cross-lingual structured prediction tasks.

Resumen

The paper introduces a novel label projection approach called Contextual Label Projection (CLaP) for cross-lingual structured prediction tasks.

Key highlights:

Label projection is essential for leveraging machine translation to facilitate cross-lingual transfer in structured prediction tasks, as it involves jointly translating labels and text.
Prior label projection methods either compromise translation accuracy by favoring simplified label translation or rely solely on word-level alignments, leading to inaccurate label translations.
CLaP translates the input text using a machine translation model and then performs contextual translation on the labels using the translated text as the context, ensuring better accuracy for the translated labels.
CLaP leverages instruction-tuned language models with multilingual capabilities as the contextual translator, imposing the constraint of the presence of translated labels in the translated text via instructions.
Experiments on two representative structured prediction tasks - event argument extraction (EAE) and named entity recognition (NER) - show that CLaP outperforms other label projection techniques, achieving over 2.4 F1 improvement for EAE and 1.4 F1 improvement for NER across 39 languages.
CLaP also demonstrates strong applicability for extremely low-resource languages, and the use of larger language models further improves performance for these languages.

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Citas

"Label projection, which involves obtaining translated labels and texts jointly, is essential for leveraging machine translation to facilitate cross-lingual transfer in structured prediction tasks."
"Prior research exploring label projection often compromise translation accuracy by favoring simplified label translation or relying solely on word-level alignments."

Ideas clave extraídas de

Contextual Label Projection for Cross-Lingual Structure Extraction

by Tanmay Parek... a las arxiv.org 04-03-2024

https://arxiv.org/pdf/2309.08943.pdf

Contextual Label Projection for Cross-Lingual Structure Extraction

Consultas más profundas

How can CLaP be extended to other structured prediction tasks beyond EAE and NER?

CLaP can be extended to other structured prediction tasks by following a similar approach of contextual label projection using instruction-tuned language models. The key steps to extend CLaP to other tasks include:

Task-specific Label Definitions: Define the specific labels and roles for the new structured prediction task. This step is crucial to ensure accurate translation of the labels in the target language.

Data Preprocessing: Preprocess the training data for the new task, including sentence-label pairs in the source language.

Model Adaptation: Fine-tune the instruction-tuned language model on the new task-specific data to adapt it to the nuances and requirements of the task.

Contextual Translation: Utilize the fine-tuned model to perform contextual translation of the labels based on the translated input sentences, ensuring accuracy and faithfulness in the label projections.

Evaluation and Iteration: Evaluate the performance of CLaP on the new task, fine-tune as necessary based on the results, and iterate to improve the model's effectiveness for the specific structured prediction task.

By following these steps and customizing CLaP for the requirements of the new structured prediction task, it can be effectively extended beyond EAE and NER to a wide range of tasks.

How can the potential limitations of using instruction-tuned language models for contextual translation be addressed?

While instruction-tuned language models offer benefits for contextual translation in label projection tasks, they also come with potential limitations that need to be addressed:

Limited Language Coverage: Instruction-tuned models may not have equal proficiency in all languages, leading to varying translation quality across languages. This limitation can be addressed by training the model on more diverse and representative data to improve language coverage.

Bias and Harmful Content: Since instruction-tuned models are not trained on filtered safe content data, there is a risk of generating biased or harmful content. To address this, additional ethical considerations and bias mitigation techniques should be implemented during model training and deployment.

Model Interpretability: Instruction-tuned models may lack interpretability, making it challenging to understand the reasoning behind their translations. Addressing this limitation involves incorporating explainability techniques to enhance model transparency and trustworthiness.

Resource Intensiveness: Training and fine-tuning instruction-tuned models can be resource-intensive. To address this, efficient training strategies, model compression techniques, and optimization methods can be employed to reduce computational costs.

By actively addressing these potential limitations through careful model development, training, and deployment practices, the effectiveness and ethical considerations of using instruction-tuned language models for contextual translation can be improved.

How can the performance of CLaP be further improved for extremely low-resource languages by leveraging additional techniques beyond larger language models?

To enhance the performance of CLaP for extremely low-resource languages, several additional techniques can be leveraged beyond larger language models:

Data Augmentation: Implement data augmentation techniques such as back-translation, synthetic data generation, and data synthesis to increase the diversity and quantity of training data for low-resource languages.

Transfer Learning: Utilize transfer learning approaches by pre-training CLaP on related tasks or languages with more data and then fine-tuning it on the low-resource languages. This helps in transferring knowledge and improving performance.

Multimodal Fusion: Incorporate multimodal data sources such as images, audio, or video along with text data to provide additional context and improve the accuracy of label projections in low-resource languages.

Active Learning: Implement active learning strategies to intelligently select and annotate the most informative data points for training CLaP, optimizing the model's performance with limited labeled data.

Domain Adaptation: Perform domain adaptation by fine-tuning CLaP on domain-specific data from the low-resource languages to enhance its performance in specific domains or contexts.

By combining these additional techniques with CLaP and customizing them for extremely low-resource languages, the model's performance can be further improved, enabling effective cross-lingual structured prediction in challenging linguistic scenarios.