Most research in Relation Extraction (RE) involves the English language,
mainly due to the lack of multi-lingual resources. We propose Multi-CrossRE,
the broadest multi-lingual dataset for RE, including 26 languages in addition
to English, and covering six text domains. Multi-CrossRE is a machine
translated version of CrossRE (Bassignana and Plank, 2022), with a sub-portion
including more than 200 sentences in seven diverse languages checked by native
speakers. We run a baseline model over the 26 new datasets and--as sanity
check--over the 26 back-translations to English. Results on the back-translated
data are consistent with the ones on the original English CrossRE, indicating
high quality of the translation and the resulting dataset.

本研究提出了多语言关系抽取（RE）数据集 Multi-CrossRE，涵盖了英语以外的 26 种语言和六种文本领域，并运用基线模型验证其高质量的机器翻译结果。

多语言多领域关系抽取数据集 Multi-CrossRE

Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation  Extraction

New smartphone users have difficulty engaging with it and often use only a
limited set of features like calling and messaging. These users are hesitant to
explore using the smartphone and rely on experienced users to teach them how to
use the phone. However, experienced users are not always around to guide them.
To help new users learn how to use the phone on their own, we propose a natural
language based instruction following agent that operates over the UI and shows
the user how to perform various tasks. Common how-to questions, such as "How to
block calls from unknown numbers?", are documented on support sites with a
sequence of steps in natural language describing what the user should do. We
parse these steps using Large Language Models (LLMs) and generate macros that
can be executed on-device when the user asks a query. To evaluate this agent,
we introduce UGIF-DataSet, a multi-lingual, multi-modal UI grounded dataset for
step-by-step task completion on the smartphone. It contains 523 natural
language instructions with paired sequences of multilingual UI screens and
actions that show how to execute the task in eight languages. We compare the
performance of different large language models including PaLM, GPT3, etc. and
find that the end-to-end task completion success rate is 48% for English UI but
the performance drops to 32% for non-English languages. We analyse the common
failure modes of existing models on this task and point out areas for
improvement.

为帮助新手学习如何自己使用智能手机，我们提出了一种基于自然语言的指导代理，然后使用大型语言模型解析自然语言说明并生成可以在设备上执行的宏。我们介绍了一个多语言、多模式的 UI 基础数据集 UGIF-DataSet，我们对不同的大型语言模型的性能进行比较，发现任务完成成功率为英语 UI 为 48%，非英语语言的性能下降到 32%。

UGIF: UI 基础指令跟踪

UGIF: UI Grounded Instruction Following

Multiple business scenarios require an automated generation of descriptive
human-readable text from structured input data. Hence, fact-to-text generation
systems have been developed for various downstream tasks like generating soccer
reports, weather and financial reports, medical reports, person biographies,
etc. Unfortunately, previous work on fact-to-text (F2T) generation has focused
primarily on English mainly due to the high availability of relevant datasets.
Only recently, the problem of cross-lingual fact-to-text (XF2T) was proposed
for generation across multiple languages alongwith a dataset, XALIGN for eight
languages. However, there has been no rigorous work on the actual XF2T
generation problem. We extend XALIGN dataset with annotated data for four more
languages: Punjabi, Malayalam, Assamese and Oriya. We conduct an extensive
study using popular Transformer-based text generation models on our extended
multi-lingual dataset, which we call XALIGNV2. Further, we investigate the
performance of different text generation strategies: multiple variations of
pretraining, fact-aware embeddings and structure-aware input encoding. Our
extensive experiments show that a multi-lingual mT5 model which uses fact-aware
embeddings with structure-aware input encoding leads to best results on average
across the twelve languages. We make our code, dataset and model publicly
available, and hope that this will help advance further research in this
critical area.

本论文提出了跨语言机器自动事实到文本生成的方法，使用包括 BERT 和 GPT-2 在内的 Transformer-based 模型和多语言数据集 XALIGNV2，得到了优秀的结果。

XF2T: 跨语言低资源事实到文本生成

XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages

There has been a recent spike in interest in multi-modal Language and Vision
problems. On the language side, most of these models primarily focus on English
since most multi-modal datasets are monolingual. We try to bridge this gap with
a zero-shot approach for learning multi-modal representations using
cross-lingual pre-training on the text side. We present a simple yet practical
approach for building a cross-lingual image retrieval model which trains on a
monolingual training dataset but can be used in a zero-shot cross-lingual
fashion during inference. We also introduce a new objective function which
tightens the text embedding clusters by pushing dissimilar texts from each
other. Finally, we introduce a new 1K multi-lingual MSCOCO2014 caption test
dataset (XTD10) in 7 languages that we collected using a crowdsourcing
platform. We use this as the test set for evaluating zero-shot model
performance across languages. XTD10 dataset is made publicly available here:
this https URL

该论文介绍了一种使用跨语言预训练进行零 - shot 学习的多模式语言和视觉问题解决方法，它可以用于构建跨语言图像检索模型和改进文本嵌入聚类，并在多语言环境下进行评估。