Crawling parallel texts $\unicode{x2014}$texts that are mutual
translations$\unicode{x2014}$ from the Internet is usually done following a
brute-force approach: documents are massively downloaded in an unguided
process, and only a fraction of them end up leading to actual parallel content.
In this work we propose a smart crawling method that guides the crawl towards
finding parallel content more rapidly. Our approach builds on two different
models: one that infers the language of a document from its URL, and another
that infers whether a pair of URLs link to parallel documents. We evaluate both
models in isolation and their integration into a crawling tool. The results
demonstrate the individual effectiveness of both models and highlight that
their combination enables the early discovery of parallel content during
crawling, leading to a reduction in the amount of downloaded documents deemed
useless, and yielding a greater quantity of parallel documents compared to
conventional crawling approaches.

通过智能爬虫方法，从互联网上下载的大量文档中更快地发现平行内容，减少无用文档的下载量，并与传统爬虫方法相比，获得更多的平行文档。

智能双语并行文档聚焦抓取

Smart Bilingual Focused Crawling of Parallel Documents

We compare sequential fine-tuning with a model for multi-task learning in the
context where we are interested in boosting performance on two tasks, one of
which depends on the other. We test these models on the FigLang2022 shared task
which requires participants to predict language inference labels on figurative
language along with corresponding textual explanations of the inference
predictions. Our results show that while sequential multi-task learning can be
tuned to be good at the first of two target tasks, it performs less well on the
second and additionally struggles with overfitting. Our findings show that
simple sequential fine-tuning of text-to-text models is an extraordinarily
powerful method for cross-task knowledge transfer while simultaneously
predicting multiple interdependent targets. So much so, that our best model
achieved the (tied) highest score on the task.

本研究比较了顺序微调和多任务学习模型在两个相关任务上性能提升的情况，并在 FigLang2022 整合任务上测试了这些模型。结果表明，顺序微调在交叉任务知识转移方面表现优异，甚至达到最高得分。