Terminology correctness is important in the downstream application of machine
translation, and a prevalent way to ensure this is to inject terminology
constraints into a translation system. In our submission to the WMT 2023
terminology translation task, we adopt a translate-then-refine approach which
can be domain-independent and requires minimal manual efforts. We annotate
random source words with pseudo-terminology translations obtained from word
alignment to first train a terminology-aware model. Further, we explore two
post-processing methods. First, we use an alignment process to discover whether
a terminology constraint has been violated, and if so, we re-decode with the
violating word negatively constrained. Alternatively, we leverage a large
language model to refine a hypothesis by providing it with terminology
constraints. Results show that our terminology-aware model learns to
incorporate terminologies effectively, and the large language model refinement
process can further improve terminology recall.

在机器翻译的下游应用中，术语的正确性非常重要，通过将术语约束注入翻译系统可以实现。本研究采用一种翻译后再优化的方法，可以实现跨领域且需要较少手动操作。我们通过使用从词对齐中获得的伪术语翻译来注释随机源词，首先训练一个术语感知模型。此外，我们还探索了两种后处理方法。第一，我们使用对齐过程来发现是否违反了术语约束，如果是，则使用负面约束对违反术语的词进行重新解码。或者，我们利用大型语言模型通过提供术语约束来优化假设。结果表明，我们的术语感知模型能够有效地学习并整合术语，而大型语言模型优化过程可以进一步提高术语的召回率。

带有约束解码和大型语言模型提示的术语感知翻译

Terminology-Aware Translation with Constrained Decoding and Large  Language Model Prompting

This paper presents a plug-and-play approach for translation with terminology
constraints. Terminology constraints are an important aspect of many modern
translation pipelines. In both specialized domains and newly emerging domains
(such as the COVID-19 pandemic), accurate translation of technical terms is
crucial. Recent approaches often train models to copy terminologies from the
input into the output sentence by feeding the target terminology along with the
input. But this requires expensive training whenever the underlying language
model is changed or the system should specialize to a new domain. We propose
Cascade Beam Search, a plug-and-play terminology-forcing approach that requires
no training. Cascade Beam Search has two parts: 1) logit manipulation to
increase the probability of target terminologies and 2) a cascading beam setup
based on grid beam search, where beams are grouped by the number of
terminologies they contain. We evaluate the performance of our approach by
competing against the top submissions of the WMT21 terminology translation
task. Our plug-and-play approach performs on par with the winning submissions
without using a domain-specific language model and with no additional training.

本文提出一种插拔式方法，用于具有术语限制的翻译，其中包括级联光束搜索，该方法不需要专门领域的语言模型和额外的训练，并获得了与 WMT21 术语翻译任务的获胜提交相当的性能。