Quality Estimation (QE), the evaluation of machine translation output without
the need of explicit references, has seen big improvements in the last years
with the use of neural metrics. In this paper we analyze the viability of using
QE metrics for filtering out bad quality sentence pairs in the training data of
neural machine translation systems~(NMT). While most corpus filtering methods
are focused on detecting noisy examples in collections of texts, usually huge
amounts of web crawled data, QE models are trained to discriminate more
fine-grained quality differences. We show that by selecting the highest quality
sentence pairs in the training data, we can improve translation quality while
reducing the training size by half. We also provide a detailed analysis of the
filtering results, which highlights the differences between both approaches.

使用质量评估（QE）指标过滤训练数据的句子对可以提高翻译质量并减少训练规模一半。

没有比更好的数据更好的数据：使用质量度量对 MT 数据进行过滤

There's no Data Like Better Data: Using QE Metrics for MT Data Filtering

Quality Estimation (QE) is the task of evaluating the quality of a
translation when reference translation is not available. The goal of QE aligns
with the task of corpus filtering, where we assign the quality score to the
sentence pairs present in the pseudo-parallel corpus. We propose a Quality
Estimation based Filtering approach to extract high-quality parallel data from
the pseudo-parallel corpus. To the best of our knowledge, this is a novel
adaptation of the QE framework to extract quality parallel corpus from the
pseudo-parallel corpus. By training with this filtered corpus, we observe an
improvement in the Machine Translation (MT) system's performance by up to 1.8
BLEU points, for English-Marathi, Chinese-English, and Hindi-Bengali language
pairs, over the baseline model. The baseline model is the one that is trained
on the whole pseudo-parallel corpus. Our Few-shot QE model transfer learned
from the English-Marathi QE model and fine-tuned on only 500 Hindi-Bengali
training instances, shows an improvement of up to 0.6 BLEU points for
Hindi-Bengali language pair, compared to the baseline model. This demonstrates
the promise of transfer learning in the setting under discussion. QE systems
typically require in the order of (7K-25K) of training data. Our Hindi-Bengali
QE is trained on only 500 instances of training that is 1/40th of the normal
requirement and achieves comparable performance. All the scripts and datasets
utilized in this study will be publicly available.

本文提出了一种基于质量评估的过滤方法，以从伪平行语料库中提取高质量的平行数据，并通过使用过滤后的语料库进行训练，改善了机器翻译系统的性能。该方法在英语 - 马拉地语、中文 - 英语和印地语 - 孟加拉语语言对中表现出了很好的效果，通过迁移学习在仅使用 500 个印地语 - 孟加拉语训练实例的情况下，相比基准模型提高了最多 0.6 个 BLEU 分数。

少即是多”：“少样本” 质量评估基于语料库过滤提升机器翻译

"A Little is Enough": Few-Shot Quality Estimation based Corpus Filtering  improves Machine Translation

In this paper, we show that the combination of Phrase Pair Injection and
Corpus Filtering boosts the performance of Neural Machine Translation (NMT)
systems. We extract parallel phrases and sentences from the pseudo-parallel
corpus and augment it with the parallel corpus to train the NMT models. With
the proposed approach, we observe an improvement in the Machine Translation
(MT) system for 3 low-resource language pairs, Hindi-Marathi, English-Marathi,
and English-Pashto, and 6 translation directions by up to 2.7 BLEU points, on
the FLORES test data. These BLEU score improvements are over the models trained
using the whole pseudo-parallel corpus augmented with the parallel corpus.

本论文表明，短语注入和语料库过滤的组合提高了神经机器翻译 (NMT) 系统的性能。我们从伪平行语料库中提取并增广平行短语和句子，以此训练 NMT 模型。在 Hindi-Marathi、English-Marathi 和 English-Pashto 这 3 种低资源语言对和 6 种翻译方向上，我们观察到了 NMT 系统的改进，并在 FLORES 测试数据上提高了 2.7 BLEU 分，这些 BLEU 分数改进是基于使用整个伪平行语料库和平行语料库增广的模型。

利用短语对注入和语料过滤改进机器翻译

Improving Machine Translation with Phrase Pair Injection and Corpus Filtering

Identifying semantically equivalent sentences is important for many
cross-lingual and mono-lingual NLP tasks. Current approaches to semantic
equivalence take a loose, sentence-level approach to "equivalence," despite
previous evidence that fine-grained differences and implicit content have an
effect on human understanding (Roth and Anthonio, 2021) and system performance
(Briakou and Carpuat, 2021). In this work, we introduce a novel, more sensitive
method of characterizing semantic equivalence that leverages Abstract Meaning
Representation graph structures. We develop an approach, which can be used with
either gold or automatic AMR annotations, and demonstrate that our solution is
in fact finer-grained than existing corpus filtering methods and more accurate
at predicting strictly equivalent sentences than existing semantic similarity
metrics. We suggest that our finer-grained measure of semantic equivalence
could limit the workload in the task of human post-edited machine translation
and in human evaluation of sentence similarity.

本篇研究提出了一种新方法用于识别句子间的语义等效性，该方法基于抽象意义表示图结构，并展示了这种方法在语义相似度方面的精度更高，认为这有助于减轻人类对机器翻译的后期编辑和对句子相似度的人工评估的负担。