In this work, we introduce sequence-to-sequence language resources for
Catalan, a moderately under-resourced language, towards two tasks, namely:
Summarization and Machine Translation (MT). We present two new abstractive
summarization datasets in the domain of newswire. We also introduce a parallel
Catalan-English corpus, paired with three different brand new test sets.
Finally, we evaluate the data presented with competing state of the art models,
and we develop baselines for these tasks using a newly created Catalan BART. We
release the resulting resources of this work under open license to encourage
the development of language technology in Catalan.

本文介绍了针对加泰罗尼亚语这种中等资源语言的序列到序列（sequence-to-sequence）语言资源，旨在实现新闻摘要和机器翻译（MT）两个任务。我们在新闻领域中介绍了两个新的抽象摘要数据集，并引入了一组新的加泰罗尼亚 - 英文平行语料库及三个不同的测试集。最后，我们评估了这些数据与最先进的模型的竞争情况，并使用新创建的加泰罗尼亚 BART 为这些任务开发了基线。我们以开放许可发布了这项工作的结果，以鼓励在加泰罗尼亚语的语言技术开发方面的进一步发展。

加泰罗尼亚文的序列到序列资源

Sequence-to-Sequence Resources for Catalan

Multilingual language models have been a crucial breakthrough as they
considerably reduce the need of data for under-resourced languages.
Nevertheless, the superiority of language-specific models has already been
proven for languages having access to large amounts of data. In this work, we
focus on Catalan with the aim to explore to what extent a medium-sized
monolingual language model is competitive with state-of-the-art large
multilingual models. For this, we: (1) build a clean, high-quality textual
Catalan corpus (CaText), the largest to date (but only a fraction of the usual
size of the previous work in monolingual language models), (2) train a
Transformer-based language model for Catalan (BERTa), and (3) devise a thorough
evaluation in a diversity of settings, comprising a complete array of
downstream tasks, namely, Part of Speech Tagging, Named Entity Recognition and
Classification, Text Classification, Question Answering, and Semantic Textual
Similarity, with most of the corresponding datasets being created ex novo. The
result is a new benchmark, the Catalan Language Understanding Benchmark (CLUB),
which we publish as an open resource, together with the clean textual corpus,
the language model, and the cleaning pipeline. Using state-of-the-art
multilingual models and a monolingual model trained only on Wikipedia as
baselines, we consistently observe the superiority of our model across tasks
and settings.

本研究集中关注在 Catalan 语言，通过构建一个中等大小的单语言模型并进行完整的评估，成功地通过多样的任务比较，充分证明了其优越性，从而证实了在数据充足的情况下，单语言特定模型的效果能够超越多语言模型。

多语种模型是否是中度资源语言的最佳选择？以加泰罗尼亚语为例的全面评估

Are Multilingual Models the Best Choice for Moderately Under-resourced  Languages? A Comprehensive Assessment for Catalan

Political identity is often manifested in language variation, but the
relationship between the two is still relatively unexplored from a quantitative
perspective. This study examines the use of Catalan, a language local to the
semi-autonomous region of Catalonia in Spain, on Twitter in discourse related
to the 2017 independence referendum. We corroborate prior findings that
pro-independence tweets are more likely to include the local language than
anti-independence tweets. We also find that Catalan is used more often in
referendum-related discourse than in other contexts, contrary to prior findings
on language variation. This suggests a strong role for the Catalan language in
the expression of Catalonian political identity.

该研究考察了推特上有关 2017 年独立公投讨论中的加泰罗尼亚语使用情况，发现亲独立推文比反对独立的推文更有可能使用当地语言，而且加泰罗尼亚语在公投有关的话题中使用频率更高。这表明加泰罗尼亚语在表达该地区政治认同方面具有强烈的作用。