It is relatively easy to mine a large parallel corpus for any machine
learning task, such as speech-to-text or speech-to-speech translation. Although
these mined corpora are large in volume, their quality is questionable. This
work shows that the simplest filtering technique can trim down these big, noisy
datasets to a more manageable, clean dataset. We also show that using this
clean dataset can improve the model's performance, as in the case of the
multilingual-to-English Speech Translation (ST) model, where, on average, we
obtain a 4.65 BLEU score improvement.

通过使用最简单的筛选技术可以从大量的嘈杂数据集中减少数据量，得到一组更易管理和干净的数据集，并且在多语言到英语语音翻译模型中使用这样的清洁数据集可以显著提高模型性能，平均提升 4.65 个 BLEU 得分。

关于端到端语音翻译过滤的案例研究

A Case Study on Filtering for End-to-End Speech Translation

In this work, we present an unsupervised retrieval method with contrastive
learning on web anchors. The anchor text describes the content that is
referenced from the linked page. This shows similarities to search queries that
aim to retrieve pertinent information from relevant documents. Based on their
commonalities, we train an unsupervised dense retriever, Anchor-DR, with a
contrastive learning task that matches the anchor text and the linked document.
To filter out uninformative anchors (such as ``homepage'' or other functional
anchors), we present a novel filtering technique to only select anchors that
contain similar types of information as search queries. Experiments show that
Anchor-DR outperforms state-of-the-art methods on unsupervised dense retrieval
by a large margin (e.g., by 5.3% NDCG@10 on MSMARCO). The gain of our method is
especially significant for search and question answering tasks. Our analysis
further reveals that the pattern of anchor-document pairs is similar to that of
search query-document pairs. Code available at
this https URL

本文研究了一种基于对比学习的无监督检索方法，应用于网络锚点文本，提出了一种去除非信息锚点的筛选技术，并展示了该方法的性能优于目前的先进方法，特别适用于搜索和问答任务。