Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models

Fuente: arXiv
Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Wang, Minghan, Vu, Thuy-Trang, Wang, Yuxia, Shareghi, Ehsan, Haffari, Gholamreza
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
_version_ 1866918204371107840
author Wang, Minghan
Vu, Thuy-Trang
Wang, Yuxia
Shareghi, Ehsan
Haffari, Gholamreza
author_facet Wang, Minghan
Vu, Thuy-Trang
Wang, Yuxia
Shareghi, Ehsan
Haffari, Gholamreza
contents Simultaneous machine translation (SimulMT) presents a challenging trade-off between translation quality and latency. Recent studies have shown that LLMs can achieve good performance in SimulMT tasks. However, this often comes at the expense of high inference cost and latency. In this paper, we propose a conversational SimulMT framework to enhance the inference efficiency of LLM-based SimulMT through multi-turn-dialogue-based decoding. Our experiments with Llama2-7b-chat on two SimulMT benchmarks demonstrate the superiority of LLM in translation quality while achieving comparable computational latency to specialized SimulMT models.
format Preprint
id arxiv_https___arxiv_org_abs_2402_10552
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models
Wang, Minghan
Vu, Thuy-Trang
Wang, Yuxia
Shareghi, Ehsan
Haffari, Gholamreza
Computation and Language
Simultaneous machine translation (SimulMT) presents a challenging trade-off between translation quality and latency. Recent studies have shown that LLMs can achieve good performance in SimulMT tasks. However, this often comes at the expense of high inference cost and latency. In this paper, we propose a conversational SimulMT framework to enhance the inference efficiency of LLM-based SimulMT through multi-turn-dialogue-based decoding. Our experiments with Llama2-7b-chat on two SimulMT benchmarks demonstrate the superiority of LLM in translation quality while achieving comparable computational latency to specialized SimulMT models.
title Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models
topic Computation and Language
url https://arxiv.org/abs/2402.10552