While Indic NLP has made rapid advances recently in terms of the availability
of corpora and pre-trained models, benchmark datasets on standard NLU tasks are
limited. To this end, we introduce IndicXNLI, an NLI dataset for 11 Indic
languages. It has been created by high-quality machine translation of the
original English XNLI dataset and our analysis attests to the quality of
IndicXNLI. By finetuning different pre-trained LMs on this IndicXNLI, we
analyze various cross-lingual transfer techniques with respect to the impact of
the choice of language models, languages, multi-linguality, mix-language input,
etc. These experiments provide us with useful insights into the behaviour of
pre-trained models for a diverse set of languages.

本文介绍了 IndicXNLI，一个 NLI 数据集，用于 11 种印度语言的跨语言转移技术的分析，研究了不同的预训练模型、语言、多语言和混合语言输入等因素对预训练模型的行为的影响。

IndicXNLI: 评估印度语言的多语言推理

IndicXNLI: Evaluating Multilingual Inference for Indian Languages

Pre-trained LMs have shown impressive performance on downstream NLP tasks,
but we have yet to establish a clear understanding of their sophistication when
it comes to processing, retaining, and applying information presented in their
input. In this paper we tackle a component of this question by examining
robustness of models' ability to deploy relevant context information in the
face of distracting content. We present models with cloze tasks requiring use
of critical context information, and introduce distracting content to test how
robustly the models retain and use that critical information for prediction. We
also systematically manipulate the nature of these distractors, to shed light
on dynamics of models' use of contextual cues. We find that although models
appear in simple contexts to make predictions based on understanding and
applying relevant facts from prior context, the presence of distracting but
irrelevant content has clear impact in confusing model predictions. In
particular, models appear particularly susceptible to factors of semantic
similarity and word position. The findings are consistent with the conclusion
that LM predictions are driven in large part by superficial contextual cues,
rather than by robust representations of context meaning.

本研究通过让模型处理带有分散注意力内容的填空任务，检验了预训练语言模型利用关键上下文信息的能力；研究结果表明，模型往往只是依靠表面上与上下文有关的信息进行预测，而对于上下文语境的理解能力不及预期。

噪声排除：测试预训练语言模型信息处理的鲁棒性

Sorting through the noise: Testing robustness of information processing  in pre-trained language models

Word alignment over parallel corpora has a wide variety of applications,
including learning translation lexicons, cross-lingual transfer of language
processing tools, and automatic evaluation or analysis of translation outputs.
The great majority of past work on word alignment has worked by performing
unsupervised learning on parallel texts. Recently, however, other work has
demonstrated that pre-trained contextualized word embeddings derived from
multilingually trained language models (LMs) prove an attractive alternative,
achieving competitive results on the word alignment task even in the absence of
explicit training on parallel data. In this paper, we examine methods to marry
the two approaches: leveraging pre-trained LMs but fine-tuning them on parallel
text with objectives designed to improve alignment quality, and proposing
methods to effectively extract alignments from these fine-tuned models. We
perform experiments on five language pairs and demonstrate that our model can
consistently outperform previous state-of-the-art models of all varieties. In
addition, we demonstrate that we are able to train multilingual word aligners
that can obtain robust performance on different language pairs. Our aligner,
AWESOME (Aligning Word Embedding Spaces of Multilingual Encoders), with
pre-trained models is available at this https URL

该论文探讨了一种嫁接预训练语言模型和平行文本的方法来提高单词对齐质量的方法，提出了从这些微调模型中有效提取对齐信息的新方法，并证明了它们在五种语言对上优于之前的最先进模型，还演示了开源的具备预训练模型的 AWESOME 单词嵌入式多语言编码对齐器的实际性能