SiLLM: Large Language Models for Simultaneous Machine Translation
This work addresses the challenge of integrating LLMs into SiMT for improved translation quality and timing, representing an incremental advancement in machine translation methods.
The paper tackles the problem of Simultaneous Machine Translation (SiMT) by decoupling it into policy-decision and translation sub-tasks, assigning them to separate agents, with a translation agent using a Large Language Model (LLM). Experiments show that SiLLM achieves state-of-the-art performance on two datasets with minimal fine-tuning data.
Simultaneous Machine Translation (SiMT) generates translations while reading the source sentence, necessitating a policy to determine the optimal timing for reading and generating words. Despite the remarkable performance achieved by Large Language Models (LLM) across various NLP tasks, existing SiMT methods predominantly focus on conventional transformers, employing a single model to concurrently determine the policy and generate the translations. However, given the complexity of SiMT, it is challenging to effectively address both tasks with a single model. Therefore, there is a need to decouple the SiMT task into policy-decision and translation sub-tasks. We propose SiLLM, which delegates the two sub-tasks to separate agents, thereby incorporating LLM into SiMT. The policy-decision agent is managed by a conventional SiMT model, responsible for determining the translation policy. The translation agent, leveraging the capabilities of LLM, generates translation using the partial source sentence. The two agents collaborate to accomplish SiMT. To facilitate the application of token-level policies determined by conventional SiMT models to LLM, we propose a word-level policy adapted for LLM. Experiments on two datasets demonstrate that, with a small amount of data for fine-tuning LLM, SiLLM attains state-of-the-art performance.