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Constructing a Simultaneous Interpretation Corpus Using Large Language Models for Distant Language Pairs

Core Concepts
Large language models can be used to automatically construct a high-quality simultaneous interpretation corpus for distant language pairs, which can improve the quality and latency of simultaneous machine translation systems.
The paper proposes a method to automatically construct a simultaneous interpretation (SI) corpus using large language models (LLMs) for the English-Japanese language pair, which has significantly different word orders. The key highlights are: Existing SI corpora are limited in size and quality due to the challenges in manual data collection and annotation. The authors leverage LLMs to convert existing speech translation corpora into SI-style data, following the Chunk-Wise Monotonic Translation (CWMT) guideline. The resulting LLM-SI-Corpus maintains the original word order and preserves the entire source content, producing more natural translations compared to existing SI corpora. Experiments show that fine-tuning simultaneous machine translation models on the LLM-SI-Corpus improves both quality and latency, outperforming models trained on existing SI and offline translation corpora. The LLM-SI-Corpus is available as a large-scale training dataset for simultaneous machine translation research.
The LLM-SI-Corpus created by GPT-3.5 and GPT-4 contains 65,083 training, 165 development, and 511 test samples. The total cost of data creation was $20 for GPT-3.5 and $400 for GPT-4.
"To solve these problems, we propose a method to convert existing speech translation (ST) corpora into SI-style data, maintaining the original word order and preserving the entire source content using Large Language Models (LLMs)." "We demonstrate that fine-tuning SiMT models in text-to-text and speech-to-text settings with the LLM-SI-Corpus reduces latencies while maintaining the same level of quality as the models trained with offline datasets."

Deeper Inquiries

How can the LLM-SI-Corpus be extended to other language pairs beyond English-Japanese

To extend the LLM-SI-Corpus to other language pairs beyond English-Japanese, a similar methodology can be applied with adjustments for the specific linguistic characteristics of the target languages. Here are the steps to extend the corpus: Selection of Original Dataset: Choose a suitable original dataset in the target language pair that aligns with the requirements for creating a SI corpus. Ensure that the dataset includes both speech and corresponding transcriptions for accurate conversion. Prompt Design: Develop a prompt template based on the guidelines for simultaneous interpretation in the target language pair. The prompt should instruct the LLMs on how to chunk the input, translate each chunk, and concatenate the chunks into coherent sentences while maintaining the original word order. LLM Training: Fine-tune the LLMs on the selected dataset using the designed prompt. It is essential to use a pre-trained model that is proficient in the target languages to ensure accurate and high-quality translations. Evaluation and Validation: Evaluate the generated SI-style corpus using quality metrics such as BLEU, BLEURT, COMET, and COMET-QE to assess the translation quality and fidelity to the original speech content. Validate the corpus with human annotators to ensure accuracy and fluency. Scaling and Iteration: Scale up the corpus creation process by incorporating more data and refining the prompt based on feedback from evaluations. Iterate on the training process to improve the quality and coverage of the corpus for the target language pair. By following these steps and adapting the methodology to the linguistic characteristics of the specific language pair, the LLM-SI-Corpus can be successfully extended to other languages beyond English-Japanese.

What are the potential limitations or biases introduced by using LLMs to construct the SI corpus, and how can they be mitigated

Using LLMs to construct the SI corpus may introduce potential limitations and biases that need to be addressed to ensure the quality and reliability of the generated data. Here are some limitations and biases that may arise and strategies to mitigate them: Model Biases: LLMs may exhibit biases present in the training data, leading to inaccuracies or skewed translations. Mitigation: Regularly evaluate the generated corpus for biases and errors, and fine-tune the models to reduce bias through diverse training data and regularization techniques. Noise in Transcriptions: Transcriptions from LLMs may contain errors or inaccuracies, impacting the quality of the SI corpus. Mitigation: Implement post-processing steps to correct errors, validate the corpus with human annotators, and incorporate feedback loops for continuous improvement. Linguistic Nuances: LLMs may struggle with capturing subtle linguistic nuances or cultural references in the target language, affecting the naturalness of the translations. Mitigation: Provide additional context or linguistic resources to the models, incorporate domain-specific knowledge, and fine-tune the models on diverse datasets to improve linguistic accuracy. Data Imbalance: The generated corpus may have imbalances in terms of language complexity, sentence structures, or vocabulary usage, leading to biased training. Mitigation: Curate a diverse and representative dataset, balance the distribution of data categories, and apply data augmentation techniques to enhance dataset diversity. By addressing these limitations and biases through careful evaluation, validation, and model refinement, the quality and reliability of the SI corpus constructed with LLMs can be improved.

How can the proposed approach be further improved to better capture the nuances and challenges of real-world simultaneous interpretation beyond the CWMT guideline

To enhance the proposed approach for capturing the nuances and challenges of real-world simultaneous interpretation beyond the CWMT guideline, several improvements can be implemented: Contextual Understanding: Enhance the LLMs' contextual understanding by incorporating contextual embeddings, attention mechanisms, and memory modules to better capture the context of the speech and improve translation coherence. Domain Adaptation: Fine-tune the LLMs on domain-specific data relevant to simultaneous interpretation, such as legal, medical, or technical content, to improve domain-specific translation accuracy and fluency. Multi-Modal Learning: Integrate multi-modal learning techniques that combine speech, text, and visual inputs to provide a more comprehensive understanding of the content and improve translation quality in diverse scenarios. Human-in-the-Loop Validation: Implement a human-in-the-loop validation process where human interpreters review and provide feedback on the generated translations to ensure accuracy, naturalness, and adherence to interpretation standards. Adaptive Chunking Strategies: Develop adaptive chunking strategies that dynamically adjust the chunk size based on the complexity and structure of the input speech to optimize translation quality and latency. By incorporating these enhancements, the proposed approach can better address the complexities and challenges of real-world simultaneous interpretation, leading to more accurate and fluent translations that align with professional interpretation standards.