We report the result of the first edition of the WMT shared task on
Translation Suggestion (TS). The task aims to provide alternatives for specific
words or phrases given the entire documents generated by machine translation
(MT). It consists two sub-tasks, namely, the naive translation suggestion and
translation suggestion with hints. The main difference is that some hints are
provided in sub-task two, therefore, it is easier for the model to generate
more accurate suggestions. For sub-task one, we provide the corpus for the
language pairs English-German and English-Chinese. And only English-Chinese
corpus is provided for the sub-task two.
We received 92 submissions from 5 participating teams in sub-task one and 6
submissions for the sub-task 2, most of them covering all of the translation
directions. We used the automatic metric BLEU for evaluating the performance of
each submission.

WMT 共享任务中，我们获得了 92 个参与队伍提交的数据，使用 BLEU 自动评估指标对机器翻译中的翻译建议进行了评估，并提供了英德和英汉语料库来进行两个子任务的翻译建议。

WMT 2022 翻译建议共享任务发现

Findings of the WMT 2022 Shared Task on Translation Suggestion

This paper describes the joint submission of Alibaba and Soochow University,
TSMind, to the WMT 2022 Shared Task on Translation Suggestion (TS). We
participate in the English-German and English-Chinese tasks. Basically, we
utilize the model paradigm fine-tuning on the downstream tasks based on
large-scale pre-trained models, which has recently achieved great success. We
choose FAIR's WMT19 English-German news translation system and MBART50 for
English-Chinese as our pre-trained models. Considering the task's condition of
limited use of training data, we follow the data augmentation strategies
proposed by WeTS to boost our TS model performance. The difference is that we
further involve the dual conditional cross-entropy model and GPT-2 language
model to filter augmented data. The leader board finally shows that our
submissions are ranked first in three of four language directions in the Naive
TS task of the WMT22 Translation Suggestion task.

该研究使用下游任务上的模型典范微调的方式，以最近取得巨大成功的巨型规模预训练模型为基础，并采用数据增强技术和条件交叉熵模型以提高机器翻译的性能，最终在 WMT22 中 Naive TS 任务的四个语言方向中的三个中排名第一。

TSMind：阿里巴巴与 苏州大学 WMT22 翻译建议任务的提交

TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task

Translation Suggestion (TS), which provides alternatives for specific words
or phrases given the entire documents translated by machine translation (MT)
\cite{lee2021intellicat}, has been proven to play a significant role in post
editing (PE). However, there is still no publicly available data set to support
in-depth research for this problem, and no reproducible experimental results
can be followed by researchers in this community. To break this limitation, we
create a benchmark data set for TS, called \emph{WeTS}, which contains golden
corpus annotated by expert translators on four translation directions. Apart
from the human-annotated golden corpus, we also propose several novel methods
to generate synthetic corpus which can substantially improve the performance of
TS. With the corpus we construct, we introduce the Transformer-based model for
TS, and experimental results show that our model achieves State-Of-The-Art
(SOTA) results on all four translation directions, including English-to-German,
German-to-English, Chinese-to-English and English-to-Chinese. Codes and corpus
can be found at this https URL

We created a benchmark data set called WeTS, which includes human-annotated golden corpus and synthetic corpus to improve the performance of Translation Suggestion (TS) for post editing after machine translation (MT). Our Transformer-based model achieves State-Of-The-Art (SOTA) results on four translation directions: English-to-German, German-to-English, Chinese-to-English and English-to-Chinese.

WeTS: 一个翻译建议的基准测试

WeTS: A Benchmark for Translation Suggestion

We present IntelliCAT, an interactive translation interface with neural
models that streamline the post-editing process on machine translation output.
We leverage two quality estimation (QE) models at different granularities:
sentence-level QE, to predict the quality of each machine-translated sentence,
and word-level QE, to locate the parts of the machine-translated sentence that
need correction. Additionally, we introduce a novel translation suggestion
model conditioned on both the left and right contexts, providing alternatives
for specific words or phrases for correction. Finally, with word alignments,
IntelliCAT automatically preserves the original document's styles in the
translated document. The experimental results show that post-editing based on
the proposed QE and translation suggestions can significantly improve
translation quality. Furthermore, a user study reveals that three features
provided in IntelliCAT significantly accelerate the post-editing task,
achieving a 52.9\% speedup in translation time compared to translating from
scratch. The interface is publicly available at
this https URL

本文介绍了一种基于智能模型的交互式翻译界面 IntelliCAT，该界面利用两个不同粒度的质量估计模型来实现机器翻译的后编辑过程，进一步提出了一种新的翻译建议模型，能够根据上下文条件性地提供特定单词或短语的替代方案，最终可以自动地保留原始文档的样式。实验结果表明，基于所提出的质量估计和翻译建议的后编辑可以显著提高翻译质量。此外，用户研究表明，IntelliCAT 提供的三个特性显著加速了后编辑任务，相对于从头开始翻译，速度提升了 52.9％。