Lecture transcript translation helps learners understand online courses,
however, building a high-quality lecture machine translation system lacks
publicly available parallel corpora. To address this, we examine a framework
for parallel corpus mining, which provides a quick and effective way to mine a
parallel corpus from publicly available lectures on Coursera. To create the
parallel corpora, we propose a dynamic programming based sentence alignment
algorithm which leverages the cosine similarity of machine-translated
sentences. The sentence alignment F1 score reaches 96%, which is higher than
using the BERTScore, LASER, or sentBERT methods. For both English--Japanese and
English--Chinese lecture translations, we extracted parallel corpora of
approximately 50,000 lines and created development and test sets through manual
filtering for benchmarking translation performance. Through machine translation
experiments, we show that the mined corpora enhance the quality of lecture
transcript translation when used in conjunction with out-of-domain parallel
corpora via multistage fine-tuning. Furthermore, this study also suggests
guidelines for gathering and cleaning corpora, mining parallel sentences,
cleaning noise in the mined data, and creating high-quality evaluation splits.
For the sake of reproducibility, we have released the corpora as well as the
code to create them. The dataset is available at
this https URL

展示了一种利用公开的课程资料进行平行语料挖掘的框架，通过动态规划的句子对齐算法和机器翻译相似度，达到了 96% 的句子对齐 F1 分数，并通过机器翻译实验证明，挖掘到的语料能提高课程讲稿翻译的质量。

双语语料库挖掘和多阶段微调以提升讲座文稿机器翻译

Bilingual Corpus Mining and Multistage Fine-Tuning for Improving Machine  Translation of Lecture Transcripts

We introduce an architecture to learn joint multilingual sentence
representations for 93 languages, belonging to more than 30 different families
and written in 28 different scripts. Our system uses a single BiLSTM encoder
with a shared BPE vocabulary for all languages, which is coupled with an
auxiliary decoder and trained on publicly available parallel corpora. This
enables us to learn a classifier on top of the resulting embeddings using
English annotated data only, and transfer it to any of the 93 languages without
any modification. Our experiments in cross-lingual natural language inference
(XNLI dataset), cross-lingual document classification (MLDoc dataset) and
parallel corpus mining (BUCC dataset) show the effectiveness of our approach.
We also introduce a new test set of aligned sentences in 112 languages, and
show that our sentence embeddings obtain strong results in multilingual
similarity search even for low-resource languages. Our implementation, the
pre-trained encoder and the multilingual test set are available at
this https URL

该研究介绍了一种以单个 BiLSTM 编码器为基础的多语言句子表示架构，其使用共享的 BPE 词汇表来学习 93 种语言的嵌入表示，并在公开可用的平行语料库上进行了训练。使用英文注释数据进行分类器训练，可将其转移至任何一种语言，且可以在跨语言自然语言推理、文档分类和平行语料库挖掘中取得良好效果。

用于零样本跨语言传递和更多应用的大规模多语句向量化技术

Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual  Transfer and Beyond

This paper presents an effective approach for parallel corpus mining using
bilingual sentence embeddings. Our embedding models are trained to produce
similar representations exclusively for bilingual sentence pairs that are
translations of each other. This is achieved using a novel training method that
introduces hard negatives consisting of sentences that are not translations but
that have some degree of semantic similarity. The quality of the resulting
embeddings are evaluated on parallel corpus reconstruction and by assessing
machine translation systems trained on gold vs. mined sentence pairs. We find
that the sentence embeddings can be used to reconstruct the United Nations
Parallel Corpus at the sentence level with a precision of 48.9% for en-fr and
54.9% for en-es. When adapted to document level matching, we achieve a parallel
document matching accuracy that is comparable to the significantly more
computationally intensive approach of [Jakob 2010]. Using reconstructed
parallel data, we are able to train NMT models that perform nearly as well as
models trained on the original data (within 1-2 BLEU).

该研究提出了一种有效的并行语料库挖掘方法，使用双语句子嵌入进行训练，通过引入硬负例来实现。该方法是基于语义相似度的，结果表明该方法可以用于重建平行文本，从而训练出 NMT 模型，与使用原始数据训练的模型相差不大。