Natural Language Processing systems are heavily dependent on the availability
of annotated data to train practical models. Primarily, models are trained on
English datasets. In recent times, significant advances have been made in
multilingual understanding due to the steeply increasing necessity of working
in different languages. One of the points that stands out is that since there
are now so many pre-trained multilingual models, we can utilize them for
cross-lingual understanding tasks. Using cross-lingual understanding and
Natural Language Inference, it is possible to train models whose applications
extend beyond the training language. We can leverage the power of machine
translation to skip the tiresome part of translating datasets from one language
to another. In this work, we focus on improving the original XNLI dataset by
re-translating the MNLI dataset in all of the 14 different languages present in
XNLI, including the test and dev sets of XNLI using Google Translate. We also
perform experiments by training models in all 15 languages and analyzing their
performance on the task of natural language inference. We then expand our
boundary to investigate if we could improve performance in low-resource
languages such as Swahili and Urdu by training models in languages other than
English.

本篇研究通过重新翻译 14 种不同语言的 MNLI 数据集，包括 XNLI 测试和开发集，以改进原始 XNLI 数据集，并通过在 15 种不同语言中训练模型并分析其在自然语言推断任务上的表现来实现跨语言理解和自然语言处理，同时通过在英语以外的语言中训练模型来探索在资源匮乏的语言（如斯瓦希里和乌尔都语）中提高性能的可能性。

XNLI 2.0：改进 XNLI 数据集和跨语言理解（XLU）性能

XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding (XLU)

Most evaluations of attribution methods focus on the English language. In
this work, we present a multilingual approach for evaluating attribution
methods for the Natural Language Inference (NLI) task in terms of plausibility
and faithfulness properties. First, we introduce a novel cross-lingual strategy
to measure faithfulness based on word alignments, which eliminates the
potential downsides of erasure-based evaluations. We then perform a
comprehensive evaluation of attribution methods, considering different output
mechanisms and aggregation methods. Finally, we augment the XNLI dataset with
highlight-based explanations, providing a multilingual NLI dataset with
highlights, which may support future exNLP studies. Our results show that
attribution methods performing best for plausibility and faithfulness are
different.

本文提出了一种多语言方法，用于评估自然语言推理的归因方法在合理性和忠实度方面的表现，并通过单词对齐量化忠实度。认为效果最好的归因方法在合理性和忠实度方面不同，并用基于高亮的解释增强了 XNLI 数据集，提供了一个支持未来 exNLP 研究的多语言 NLI 数据集。