Exploring Translationese for Language Model Pretraining

Kernkonzepte

Using translationese as synthetic data for pre-training language models can bridge the gap in data scarcity for non-English languages.

Zusammenfassung

The content explores the use of Translationese, synthetic data created through machine translation, for pre-training language models. It discusses the challenges of data scarcity for languages other than English and presents a framework involving TinyLMs to filter synthetic data efficiently. The study shows that LMs trained on filtered synthetic data perform comparably to those trained on clean data, with additional benefits observed from extended pretraining on a small fraction of clean data. The release of IndicMonoDoc, a large collection of monolingual document-level corpora, is highlighted as a contribution to bridging the performance gap between English and non-English languages.
Abstract:

Translationese as synthetic data for LM pretraining.
Challenges of data scarcity in non-English languages.
Framework using TinyLMs to filter synthetic data efficiently.
Performance comparison between LMs trained on clean vs. synthetic data.
Benefits of extended pretraining on a small fraction of clean data.
Release of IndicMonoDoc dataset.
Introduction:

Large language models' performance credited to scale and vast amount of training data.
Data scarcity in many languages compared to English affects LM performance.
Synthetic data offers a solution to supplement resource scarcity.
Methodology:

Curation of monolingual web-crawled clean data.
Generation of translationese (synthetic) data using MT models like IndicTrans2.
Training TinyLMs on clean data and using them to filter synthetic documents based on perplexity.
Training final LMs on filtered synthetic corpora for downstream tasks.
Results:

Filtered synthetic text competitive with web-scraped clean text.
Extended pretraining with clean data boosts performance of LMs trained solely on synthetic text.
Source language selection impacts characteristics and quality of generated translationese text.
Conclusion:
The study demonstrates the feasibility and effectiveness of using translationese as synthetic data for training language models, particularly addressing the challenge of resource scarcity in non-English languages. The release of IndicMonoDoc dataset further contributes to this effort.

Statistiken

We take the case of English and Indic languages and translate web-crawled monolingual documents (clean) into the target language. Then, we train language models containing 28M and 85M parameters on this translationese data (synthetic).
Recently, there has been a growing interest in using synthetic data to address this data scarcity.
We show that their performance on downstream natural language understanding and generative tasks is only 3.56% poorer on NLU tasks and 1.51% on NLG tasks than LMs pre-trained on clean data.

Zitate

"Synthetic text obtained using machine translation can significantly enhance model performance."
"We propose a simple framework involving high-quality MT models and TinyLMs trained on clean web-crawled datasets."
"Our contributions include demonstrating the efficacy of LMs trained on filtered synthetic datasets across various downstream tasks."

Wichtige Erkenntnisse aus

Do Not Worry if You Do Not Have Data

by Meet Doshi,R... um arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13638.pdf

Tiefere Fragen

How can the use of translationese impact multilingual LM development?

The use of translationese, which involves generating synthetic data through machine translation, can have a significant impact on multilingual language model (LM) development. By leveraging translationese data, researchers can address the scarcity of training data for low-resource languages. This approach allows for the creation of large-scale datasets in multiple languages by translating existing monolingual documents. These translated datasets can then be used to pretrain LMs effectively.
Translationese plays a crucial role in enabling the training of multilingual LMs that cover a wide range of languages. It helps bridge the gap between high-resource and low-resource languages by providing sufficient training data for models to learn from. Additionally, using synthetic data generated through machine translation facilitates cross-lingual learning and improves the performance of LMs across different languages.
Furthermore, incorporating translationese into LM development pipelines enhances model generalization capabilities across various linguistic contexts. It enables researchers to explore diverse language structures and patterns present in different languages, leading to more robust and versatile multilingual models.

How might advancements in MT technology influence future research directions in LM training?

Advancements in Machine Translation (MT) technology are poised to significantly influence future research directions in Language Model (LM) training. As MT systems continue to improve in terms of accuracy and efficiency, they offer new opportunities for enhancing LM development processes:

Improved Data Generation: Advanced MT models can generate high-quality synthetic data for pretraining LMs efficiently. This opens up possibilities for creating large-scale multilingual corpora by translating monolingual text into multiple languages.

Enhanced Cross-Lingual Learning: With better MT capabilities, researchers can explore more sophisticated methods for cross-lingual learning with LMs. This includes leveraging parallel corpora and back-translation techniques to train models that understand multiple languages simultaneously.

Fine-Tuning Strategies: Future research may focus on optimizing fine-tuning strategies using state-of-the-art MT technologies. Fine-tuning pretrained LMs on specific tasks or domains could benefit from advanced transfer learning techniques facilitated by improved MT systems.

4Ethical Considerations
When releasing large-scale datasets like IndicMonoDoc or any other dataset containing sensitive information or potentially harmful content:
1Data Privacy: Ensure that personal information is anonymized or removed from the dataset to protect individuals' privacy.
2Toxic Content: Implement measures such as content moderation algorithms or manual review processes to filter out toxic or inappropriate content.
3Bias Mitigation: Address biases within the dataset related to gender, race, ethnicity, etc., through careful curation and diversity considerations.
4Informed Consent: Obtain consent from contributors whose data is included in the dataset if applicable.
5Transparency: Provide clear documentation about how the dataset was collected, processed, and any potential limitations or biases it may have.

How might advancements in MT technology influence future research directions in LM training

Advancements in Machine Translation (MT) technology are expected to shape future research directions in Language Model (LM) training significantly:
1* Enhanced Multilinguality: Improved MT systems will enable better generation of synthetic multilingual datasets essential for training robust multilingual LMs capable of understanding various languages effectively.
2* Cross-Lingual Transfer Learning: Advancements in MT will facilitate more efficient cross-lingual transfer learning approaches where knowledge learned from one language can be transferred effectively to another during LM training.
3* Quality Improvement: Better quality translations produced by advanced MT models will lead to higher-quality synthetic datasets used for pretraining LMS resultingin enhanced overall performance
4* Resource Efficiency: More accurate translations provided by advanced MTSystems reduce noise introduced during synthesis making them ideal resourcesfor effective LMtraining
5* Ethical Implications: Researchers must consider ethical implications associated with using synthesizeddata including ensuring fairnessand avoiding bias when utilizing thesedatasetsfor developingLMS
These developments underscorethe importanceof integrating cutting-edgeMTtechnologiesintoLMresearchtoenhancemodelperformanceacrosslanguagesandpromotecross-lingualeffectivenessinNLPapplications